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Sentiment and hashtag-aware attentive deep neural network for multimodal post popularity prediction

Neural Computing and Applications 2024年第4期37卷 2799-2824页

作者： Bansal, Shubhi Kumar, Mohit Raghaw, Chandravardhan Singh Kumar, Nagendra Department of Computer Science and Engineering Indian Institute of Technology Indore Khandwa Road Simrol Madhya Pradesh Indore453552 India

Social media users articulate their opinions on a broad spectrum of subjects and share their experiences through posts comprising multiple modes of expression, leading to a notable surge in such multimodal content on social media platforms. Nonetheless, accurately forecasting the popularity of these posts presents a considerable challenge. Prevailing methodologies primarily center on the content itself, thereby overlooking the wealth of information encapsulated within alternative modalities such as visual demographics, sentiments conveyed through hashtags and adequately modeling the intricate relationships among hashtags, texts, and accompanying images. This oversight limits the ability to capture emotional connection and audience relevance, significantly influencing post popularity. To address these limitations, we propose a seNtiment and hAshtag-aware attentive deep neuRal netwoRk for multimodAl posT pOpularity pRediction, herein referred to as NARRATOR that extracts visual demographics from faces appearing in images and discerns sentiment from hashtag usage, providing a more comprehensive understanding of factors influencing post popularity. Moreover, we introduce a hashtag-guided attention mechanism that leverages hashtags as navigational cues, guiding the model’s focus toward the most pertinent features of textual and visual modalities, thus aligning with target audience interests and broader social media context. Experimental results demonstrate that NARRATOR outperforms existing methods by a significant margin on two real-world datasets. Furthermore, ablation studies underscore the efficacy of integrating visual demographics, sentiment analysis of hashtags, and hashtag-guided attention mechanisms in enhancing the performance of post popularity prediction, thereby facilitating increased audience relevance, emotional engagement, and aesthetic appeal. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Deep neural networks

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Multivariate Time series Forecasting for Cloud Machine Utilization 3

Multivariate Time series Forecasting for Cloud Machine Utili...

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3rd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Naik, Archana Sooda, Kavitha Department of Computer Science and Engineering Nitte Meenakshi Institute of Technology Bangalore India Department of Computer Science and Engineering B.M.S. College of Engineering Bangalore India

ISBN: (纸本)9798331527549

The cloud machine utilization prediction using a multivariate time series model is explored in this study. The cloud machine usage data set samples are selected with varied patterns. The long sequence forecasting model Informer is investigated for the forecasting. The model results are benchmarked against LSTM, GRU, Prophet, and XGBoost across the diverse datasets. The performance is evaluated using RMSE. Experimental results show that the Prophet model performs best overall due to its ability to capture both seasonal and irregular patterns accurately. The RMSE value for Informer is 0.13, Prophet is 0.097, XGBoost is 0.096, LSTM is 0.1725, and GRU is 0.1547. © 2025 IEEE.

关键词： Time series

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The Multi-Way Path Over the Blockchain: The Development of the Security Algorithm RB-24-1 6

The Multi-Way Path Over the Blockchain: The Development of t...

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6th International Conference on Mobile Computing and Sustainable Informatics, ICMCSI 2025

作者： Rajaprakash, S. Bagath Basha, C. Rathish Babu, T.K.S. Karthik, S. Subapriya, V. Kavitha, S. Department of Computer Science & Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Tamil Nadu Chennai India Department of Computer Science & Engineering Kommuri Pratap Reddy Institute of Technology Telagana Hyderabad India Department of Computer Science & Engineering SRM Institute of Science and Technology Ramapuram Tamil Nadu Chennai India Department of Computer Science & Engineering Aarupadai Veedu Institute of Technology Vinayaka Missions Research Foundation Tamil Nadu Chennai India Department of Computer Science & Engineering Sathyabama Institute of Science and Technology Tamil Nadu Chennai India

ISBN: (数字)9798331522667

ISBN: (纸本)9798331522667

In the current world, a lot of new technologies are becoming famous. The Block Chain is one of these technologies. A very high amount of safety is offered by the technology in question;it is extremely robust. Users are not privy to a great deal of information regarding Block Chain;however, Protecting the data that is sent in multiple ways is what its security features are for. The "SalSa and RBJ25 algorithms" were still being used by this person. We are going to talk about the new "security mechanism" we have named RB-24-1. This method is comprised of these four different techniques. The procedure begins with the "Latin Alphabet code" being assigned to T. This is the first step in the process. To proceed to the next step, use Equation (1) to perform four times the multiplication of the number by itself. "Exchanging the values of the cells" is what the third step does., and it is carried out with the help of encrypted data. Putting the "SalSa" algorithm into action within the matrix constitutes the fourth step throughout the process. Finally, the plain text is converted into the encrypted text format. This has been a long time coming. There is a higher level of security offered by the method that has been suggested in compare and contrast with the method that is being used at the moment. © 2025 IEEE.

关键词： Encryption algorithms

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Blockchain-powered Healthcare: Revolutionizing Security and Privacy in IoT-based Systems

Blockchain-powered Healthcare: Revolutionizing Security and ...

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2024 International Conference on Computational Intelligence and Computing Applications, ICCICA 2024

作者： Singla, Deepak Kumar, Sanjeev Dhingra, Deepti Ghandhi, Anju Department Of Computer Science And Engineering Ambala Haryana Mullana India Panipat Institute Of Engineering And Technology Department Of Computer Science And Engineering Panipat Haryana Samalkha India

ISBN: (纸本)9798350306446

The rapid growth of the IoT and wearable device industries has expanded remote patient monitoring, but healthcare systems face security vulnerabilities inherent in their client/server architecture. To address these challenges and meet regulatory standards, a shift to a distributed architecture is necessary. Blockchain technology offers promise with its distributed nature and security features. MediSecureNet, proposed in this paper, integrates blockchain and IoT to create a secure healthcare system. Prioritizing security, scalability, and processing speed, MediSecureNet focuses on remote patient monitoring, particularly for chronic conditions. It incorporates a re-encryption proxy for security, blockchain for hash data storage, and smart contracts for access management. Scalability is achieved through an off-chain IPFS database, and an Ethereum-based proof of authority mechanism enhances data storage efficiency. © 2024 IEEE.

关键词： Internet of things

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Deep Q-learning based Resource Scheduling in IoT Edge Computing 7

Deep Q-learning based Resource Scheduling in IoT Edge Comput...

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7th International Conference on Inventive Computation Technologies, ICICT 2024

作者： Vijayasekaran, G. Duraipandian, M. New Horizon College of Engineering Department of Computer Science & Engineering Karnataka Bangalore India Hindusthan Institute of Technology Department of Computer Science And Engineering Coimbatore India

ISBN: (纸本)9798350359299

Edge computing (EC) has emerged as a viable solution for resource-intensive Internet of Things (IoT) applications seeking low-latency services at the network edge. However, the limited computing power of edge servers poses challenges in scheduling application jobs and allocating resources effectively. This study addresses the task scheduling and resource allocation problem in the EC scenario, aiming to maximize the long-term satisfaction of tasks assigned to virtual machines (VMs) deployed at the edge server. Formulated as a Markov decision process (MDP), the problem involves states, actions, state transitions, and rewards. Subsequently, a Deep Q Neural Network (DQN)-based resource allocation algorithm is developed to devise an optimal offloading decision and resource allocation scheme in an edge computing environment with constrained resources and latency limits. Simulation results demonstrate that the proposed DQN-based methods outperform existing approaches in the literature, achieving superior performance in terms of average waiting time and efficiency. This research contributes to enhancing the effectiveness and efficiency of resource allocation in edge computing environments, thereby facilitating the deployment of latency-sensitive IoT applications. © 2024 IEEE.

关键词： Internet of things

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On the Maximum Distance Sublattice Problem and Closest Vector Problem 26

On the Maximum Distance Sublattice Problem and Closest Vecto...

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26th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing, SYNASC 2024

作者： Kumar, Rajendra Mehta, Shashank K Rajasree, Mahesh Sreekumar Indian Institute of Technology Delhi Department of Computer Science & Engineering New Delhi India Indian Institute of Technology Kanpur Department of Computer Science & Engineering Uttar Pradesh India

ISBN: (纸本)9798331532833

In this paper, we introduce the Maximum Distance Sublattice Problem (MDSP). We observed that the problem of solving an instance of the Closest Vector Problem (CVP) in a lattice L is the same as solving an instance of MDSP in the dual lattice of L. We give an alternate reduction between the CVP and MDSP. This alternate reduction does not use the concept of dual lattice. © 2024 IEEE.

关键词： CVP geometry GSO Karp reduction lattice

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A deep learning-based early alzheimer’s disease detection using magnetic resonance images

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Multimedia Tools and Applications 2025年第16期84卷 16561-16582页

作者： Suchitra, S. Krishnasamy, Lalitha Poovaraghan, R.J. Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R &D Institute of Science and Technology Avadi Tamil Nadu Chennai India Department of Information Technology Nandha Engineering College Tamil Nadu Erode India Department of Information Technology Jaya Engineering College Tamil Nadu Thiruninravur Chennai India

Alzheimer’s Disease (AD) is a degenerative, chronic condition of the brain for which there is now no effective treatment. However, there are medications that can slow its development. In order to stop and control the development of AD, earlier diagnosis of the disease is quintessential. Our proposed method’s primary objective is to establish a comprehensive model for the prior detection of Alzheimer’s disease and the categorization of distinct AD stages. This work employs a deep learning methodology, especially CNN. The proposed approach makes use of well-known models that have already been trained to classify medical images, like the EfficentNetB7 model, by applying the transfer learning principle. In order to achieve greater accuracy, convolutional neural networks (CNNs) are frequently scaled up as new resources become available at a fixed cost throughout the construction phase. A compound coefficient is used by the CNN architecture and scaling approach, which is the foundation of the pre-trained EfficientNetB7 model, to scale the dimensions equally. This proposed EfficientNetB7 model is quicker, easier, and more effective than other pre-trained models like VGG19 and InceptionV3. The proposed model includes simple structures that have memory requirements, provide manageable time, overfitting, and low computational complexity as well as training and inference speeds. The Alzheimer’s disease Neuroimaging Initiative (ADNI) dataset was employed for a comprehensive assessment of the method proposed, utilizing well-known performance metrics including sensitivity, specificity, and accuracy. The findings revealed that the improvised results achieved the accuracy metric when compared to existing methods. The EfficientNetB7 model has been enhanced, and this model achieves a sensitivity of 98.08%, specificity-98%, accuracy-98.2%, and F-score-98.95%, for multi-class AD stage classifications. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, par

关键词： Magnetic resonance

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BDSLL: Biomedical Document Recommendation Using Knowledge-Empowered Semantic Intercepted Large Language Models 4th

BDSLL: Biomedical Document Recommendation Using Knowledge-Em...

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4th International Conference on Emerging Trends and Technologies on Intelligent Systems, ETTIS 2024

作者： Aravind Krishnan, A. Deepak, Gerard Department of Computer Science and Engineering Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Manipal India Department of Computer Science and Engineering BMS Institute of Technology and Management Bangalore India

ISBN: (纸本)9789819757022

There is a requirement for document recommendation frameworks focusing on certain domains linked to medical sciences and biosciences like biomedical document recommendation in the era of the Web 3.0. This paper proposes a biomedical document recommendation strategy that realizes informative term generation using the encompassment of extraction of terms and categories and the TF-IDF. The framework also generates an ontology from the informative terms harvested from which features are selected to classify the dataset using a random forest classifier. The model also uses very strong knowledge bases that are highly specific to the domain like QIAGEN. It also uses WikiData which is generic but has a full cover of world knowledge to bridge the knowledge present in the datasets and the existing structure web. Metadata generation also increases the quantity of additional knowledge that is encompassed in the model. BioBERT is a strategic large language model that focuses on biosciences and biomedicine as its domain is used to classify the metadata to make it more permeable into the model. Semantic similarity is computed using NPMI, web overlap, SimRank, and Ricklefs and Lau index at different stages in the pipeline to facilitate quantitative reasoning through semantic relatedness computation. The salient semantic analysis also accelerates the overall semantic association into the proposed framework. This framework can demonstrate a precision of 96.56%, recall of 96.93%, and an FDR of 0.04. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Ontology

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Robot assisted knee joint RoM exercise: A PID parallel compensator architecture through impedance estimation

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Cognitive Robotics 2024年第1期4卷 42-61页

作者： Akhtaruzzaman, M. Shafie, Amir A. Khan, Md Raisuddin Rahman, Md Mozasser Department of Computer Science and Engineering Military Institute of Science and Technology 1216 Dhaka Bangladesh Department of Mechatronics Engineering International Islamic University Malaysia Kuala Lumpur53100 Malaysia Faculty of Engineering Technology Universiti Tun Hussein Onn Malaysia 86400 Johor Malaysia

Knee joint rehabilitation exercise refers to a therapeutic procedure of a patient having dysfunctions in certain abilities to move knee joint due to some medical conditions like trauma or paralysis. The exercise is basically a series of repeated assistive physical movements within the range of motion (RoM) of the joint. Reflex action of limbs during RoM exercise causes inappropriate balance of load which may cause secondary injuries, such as damages of muscle or tendon tissues. Establishing correlation between impedance data and limb motions is important to solve this problem. This paper aims to design and modeling of a robotic arm with an original approach in control strategy which is developed based on the correlation in between the joint-impedances and joint-motion characteristics during exercise. The knee joint impedances are estimated based on the internal feedback of the system dynamics, that lead to design the torque compensator to improve the overall control signals in real time. This paper also demonstrates the characteristics of various responses of the system during exercise with human subject. Results have reflected good performances with low position and velocity tracking errors, ±0.02∘ and ***−1 during hold phase;and ±0.14∘ and ***−1 during motion phse. Though, the limitation of the prototype is its current RoM (limited to 0∘–25∘), the system has potential in the application of RoM exercise for paraplegic or monoplegic patients. © 2023

关键词： Human robot interaction

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Revolutionizing Bus Tracking and Arrival Prediction in Real Time for Both Public and Private Sectors, Powered by Firebase 3

Revolutionizing Bus Tracking and Arrival Prediction in Real ...

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3rd IEEE International Conference on Distributed Computing and Electrical Circuits and Electronics, ICDCECE 2024

作者： Shafiulla, Mohammed Abdul Lateef Haroon, P.S. Naveed, Shaik Mohammed Ahmed, Suhail Mahika Dhar, M. Kumar, Utkarsh Ballari Institute Of Technology & Management Department Of Computer Science And Engineering Ballari India Ballari Institute Of Technology And Management Department Of Electronics And Communication Engineering Ballari India Ballari Institute Of Technology And Management Department Of Computer Science And Engineering Ballari India

ISBN: (纸本)9798350318609

Public transport systems often face challenges related to reliability and efficiency, leading to inconvenience and uncertainty for passengers. Existing solutions for real-time bus monitoring typically rely on expensive Internet of Things (IoT) components. This paper presents Track N Go, a cost-effective alternative for real-time monitoring of public buses and taxis using mobile phone location data. The system utilizes a robust backend system built with Java and *** for the user interface. Integration of GPS via the Map Box API and Firebase database ensures accurate location tracking. Track N Go provides accurate real-time monitoring, addressing challenges associated with public transport and improving overall urban mobility. The system enhances convenience and peace of mind for passengers by delivering reliable and user-centric solutions. The evaluation of Track N Go demonstrates its effectiveness in improving urban mobility and transit services. By offering a reliable and cost-effective solution for real-time bus monitoring, Track N Go aims to streamline daily commutes and make public transport more efficient and predictable. © 2024 IEEE.

关键词： Urban transportation

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