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Sem-AI: A Unique Framework for Sentiment Analysis and Opinion Mining Using Social Network Data

SN computer science 2025年第2期6卷 1-23页

作者： Maruthupandi, J. Sivakumar, S. Kumar, V. Senthil Srikaanth, P. Balaji Department of Computer Science and Engineering New Horizon College of Engineering Karnataka Bangalore India Department of Computer Science and Engineering SRM Institute of Science and Technology Tamilnadu Trichy India Department of Networks and Communication SRM Institute of Science and Technology Tamilnadu Chennai India Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R & D Institute of Science and Technology Chennai India

Social media has become an essential forum for people to share their thoughts and sentiments owing to the quick rise in mobile technology. Business and political organizations might benefit from understanding public sentiment when making strategic decisions. Challenges in the area include handling noisy and incomplete data, variation in language use across domains, and integration into existing systems of models developed for analysis. In these ways, they can seriously hamper the accuracy and generalizability of sentiment analysis models. In view of the challenges, this work has developed a new and innovative framework of AI-based semantic analysis for assessing and classifying user views from social data. In spite of this, sentiment analysis is crucial for determining the opposing perspectives of the general public's sentiments. This work constructed a new and innovative framework called Semantic analysis based on AI (SemAI) for assessing and categorizing user views from social data. With high efficiency, a collection of intelligence algorithms in the stages of feature extraction, optimization, and sentiment prediction are applied here. The Degree of Correlation Network Model (DCNM) is used in this work to extract the subset of dependable and most-required features from social network data. The most recent meta-heuristics model, Heap based Optimization (HbO), is used to reduce the dimensionality of retrieved features. Furthermore, the unique classification algorithm, Self-Attention based Deep Analyzing Network (SA-DAN), is used in this work to precisely identify and categorize attitudes into positive, neutral, and negative classifications. This present study used a variety of most recent social corpus data to investigate and evaluate the Sem-AI model's outputs. The SemAI model achieved impressive results, with accuracy, precision, and F1-scores of 99%, 98.9%, and 99%, respectively, across various social corpus datasets. © The Author(s), under exclusive licence to S

关键词： Classification Degree of correlation network model (DCNM) Heap based optimization (HbO) Opinion mining Self-attention based deep analyzing network (SA-DAN) Semantic analysis Social data

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Wasserstein enabled Bayesian optimization of composite functions

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Journal of Ambient Intelligence and Humanized Computing 2023年第8期14卷 11263-11271页

作者： Candelieri, Antonio Ponti, Andrea Archetti, Francesco Department of Economics Management and Statistics University of Milano-Bicocca Milan Italy Department of Computer Science Systems and Communication University of Milano-Bicocca Milan Italy

Bayesian optimization (BO) based on the Gaussian process model (GP-BO) has become the most used approach for the global optimization of black-box functions and computationally expensive optimization problems. BO has proved its sample efficiency and its versatility in a wide range of engineering and machine learning problems. A limiting factor in its applications is the difficulty of scaling over 15–20 dimensions. In order to mitigate this drawback, it has been remarked that optimization problems can have a lower intrinsic dimensionality. Several optimization strategies, built on this observation, map the original problem into a lower dimension manifold. In this paper we take a novel approach mapping the original problem into a space of discrete probability distributions endowed with a Wasserstein metric. The Wasserstein space is a non-linear manifold whose elements are discrete probability distributions. The input of the Gaussian process is given by discrete probability distributions and the acquisition function becomes a functional in the Wasserstein space. The minimizer of the acquisition functional in the Wasserstein space is then mapped back to the original space using a neural network. Computational results for three test functions with dimensionality ranging from 5 to 100, show that the exploration in the Wasserstein space is significantly more effective than that performed by plain Bayesian optimization in the Euclidean space and its advantage grows with the dimensions of the search space. © 2023, The Author(s).

关键词： Global optimization

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6G-SECUREIDS: Blockchain-Enhanced Secure Knowledge Transfer for Distributed Intrusion Detection systems in Advanced networks 6

6G-SECUREIDS: Blockchain-Enhanced Secure Knowledge Transfer ...

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6th International Conference on Networking and Advanced systems, ICNAS 2023

作者： Sakraoui, Sabrina Derdour, Makhlouf Ahmim, Ahmed Badji Mokhtar Annaba University Networks and Systems Laboratory Department of Computer Science Annaba Algeria Oum El Bouaghi University Networks and Systems Laboratory Department of Computer Science Oum El Bouaghi Algeria Mohamed-Cherif Messaadia University Networks and Systems Laboratory Dpt. of Mathematics and Computer Science Souk Ahras Algeria

ISBN: (纸本)9798350319170

As the development of wireless networks advances towards the deployment of 6G technology, ensuring robust security measures becomes crucial. In this paper, we propose 6G-SECUREIDS, a novel intrusion detection system designed specifically for 6G wireless networks. The system leverages machine learning techniques and blockchain technology to detect and mitigate security threats, safeguard data privacy, and ensure efficient and optimized training. Our experimental results demonstrate the effectiveness and reliability of the system, with the clients achieving high accuracy scores across different model architectures. The promising results highlight the potential of 6G-SECUREIDS as a valuable security solution for future 6G networks, contributing to the overall protection and integrity of the wireless communication ecosystem. © 2023 IEEE.

关键词： Wireless networks

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Towards a Cyber Defense System in Software-Defined Tactical networks

Towards a Cyber Defense System in Software-Defined Tactical ...

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2024 International Conference on Military communication and Information systems, ICMCIS 2024

作者： Kloth, Sean Rettore, Paulo H.L. Zibner, Philipp Santos, Bruno P. Sevenich, Peter Institute of Computer Science 4 University of Bonn Bonn Germany Fraunhofer Fkie Department of Communication Systems Bonn Germany Federal University of Bahia Department of Computer Science Salvador Brazil

ISBN: (纸本)9798350373196

This study investigates the robustness of a Software-defined Networking (SDN) controller when confronted with a Distributed Denial-of-Service (DDOS) attack in a tactical environment. A proactive defense mechanism is introduced to detect and respond to a flooding of 'packet-in' requests, triggering a response once the network features indicate an anomaly. The methodology consists of two components: the Cyber Defense Agent (CDA), consisting of monitoring, feature engineering, detection, and responses, and the Cyber Attack Agent (CAA), including the preparation, execution, and evaluation of the attack. The CDA monitors all the IP flows from the SDN controller and processes four main features such as the average number of 'packet-in' requests, the response time to these requests, the entropy of IP addresses and ports for source and destination, and 'packet-in' requests per switch to identify compromised switches. All the components were emulated and tested, collecting quantitative evidence to demonstrate the effectiveness of both agents. © 2024 IEEE.

关键词： Software defined networking

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Public Key Infrastructure Approaches Based on Blockchain 21

Public Key Infrastructure Approaches Based on Blockchain

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21st International Multi-Conference on systems, Signals and Devices, SSD 2024

作者： Alani, Dena S. Sagheer, Ali M College of Education for Women University of Anbar Anbar Iraq College of Computer Science and Information Technology University of Anbar Department of Computer Networks Systems Anbar Iraq

ISBN: (纸本)9798350374131

Public-key infrastructure or PKI, is the fundamental technology that makes safe online information sharing possible. But PKI is vulnerable to failures of Certificate Authorities (CAs), which could allow illegal certificates to be issued to the end users. The security of the corresponding end users is at danger if a CA is compromised, as seen by numerous recent breaches. Blockchain technology is a new solution that has the ability to address the issues with traditional PKI systems, specifically the removal of single points of failure and the quick response to CA flaws. Blockchain technology facilitates the management and archiving of digital certificates in an unchangeable, publicly accessible ledger, producing a history log that can be completely tracked. In this article, the classical and modern blockchain-based PKI strategies were explained in addition to illustrating the limitations of each strategy. Finally, this article analyzed PKI methods by comparing their features according to a number of parameters. © 2024 IEEE.

关键词： Blockchain

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iLIAC: An approach of identifying dissimilar groups on unstructured numerical image dataset using improved agglomerative clustering technique

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Multimedia Tools and Applications 2024年第39期83卷 86359-86381页

作者： S, Sreedhar Kumar Ahmed, Syed Thouheed Fathima, Afifa Salsabil Mathivanan, Sandeep Kumar Jayagopal, Prabhu Saif, Abdu Gupta, Sachin Kumar Sinha, Garima Department of Computer Science and Engineering School of Engineering and Technology CMR University Karnataka Bengaluru India Department of Electrical Engineering Indian Institute of Technology Hyderabad Telangana Kandi India School of Computer Science and Engineering REVA University Bangalore India School of Computer Science and Engineering Galgotias University Uttar Pradesh Greater Noida203201 India School of Computer Science Engineering and Information Systems Vellore Institute of Technology Tamil Nadu Vellore632014 India Department of Communication and Computer Engineering Faculty of Engineering and IT Taiz University Taiz Yemen Katra India Department of Computer Science Engineering Jain University Bangalore India

Unstructured Numerical Image Dataset Separation (UNIDS) method employing an enhanced unsupervised clustering technique. The objective is to delineate an optimal number of distinct groups within the input grayscale (G-S) image content, utilizing the improved limited iteration agglomerative clustering (iLIAC) clustering technique for the separation and enhancement of picture elements. The UNIDS method is structured into two primary stages: partitioning and validation. In the partitioning stage, the UNIDS method identifies an appropriate number of discrete clusters within the grayscale image using the iLIAC technique, eliminating the need for predetermined procedures. Subsequently, the method evaluates the similarity and deviation among data elements within the same group in the resultant image dataset. Additionally, it assesses the proximity and inters severance among clusters in the outcome of the image dataset through the partitioning process. Empirical results indicate that the UNIDS system excels in the spontaneous identification of an optimal number of discrete clusters within the input G-S image. The system demonstrates superior thickness, reduced deviation among data elements within the same cluster, increased inter-separation, and diminished inter-closeness between cluster elements. Furthermore, empirical analysis establishes the superior performance of the UNIDS approach compared to existing clustering techniques. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Iterative methods

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Student academic success prediction in multimedia-supported virtual learning system using ensemble learning approach

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Multimedia Tools and Applications 2024年第40期83卷 87553-87578页

作者： Saidani, Oumaima Umer, Muhammad Alshardan, Amal Alturki, Nazik Nappi, Michele Ashraf, Imran Department of Information Systems College of Computer and Information Sciences and Princess Nourah bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia Department of Computer Science The Islamia University of Bahawalpur Bahawalpur Pakistan Department of Computer Science University of Salerno Fisciano84084 Italy Information and Communication Engineering Yeungnam University Gyeongsan38541 Korea Republic of

Multimedia systems and metaverse has been gaining increasing interest for education in virtual environments. With wide adoption of these technologies, the data is expected to grow exponentially. The increasing rise of educational data indicates that standard processing methods may be limited. As a result, constructing data mining-based academic performance assessment methods is becoming increasingly important. Students’ academic performance prediction can raise an alert for students at risk of failure and can help academia plan accordingly. This research suggests utilizing features derived from a convolutional neural network (CNN) in conjunction with machine learning models to enhance the overall performance of the model. By doing so, the necessity for manual feature extraction is eliminated, leading to superior outcomes compared to using machine learning and deep learning models separately. At the outset, nine machine learning models are employed to assess both the original features and the convoluted features. The top two individual models are later used to build an ensemble model using soft voting criteria. The ensemble model is built using random forest (RF) and support vector machine (SVM) for predicting student academic performance. The performance of the proposed approach is compared to existing models for validation indicating the superior performance of the former. With an accuracy of 98.99%, as well as precision, recall, and F1 scores of 99%, the proposed method achieved outstanding results for predicting student academic performance. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Learning systems

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When Computing Power Network Meets Distributed Machine Learning: An Efficient Federated Split Learning Framework 31

When Computing Power Network Meets Distributed Machine Learn...

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31st IEEE/ACM International Symposium on Quality of Service, IWQoS 2023

作者： Yuan, Xinjing Pu, Lingjun Jiao, Lei Wang, Xiaofei Yang, Meijuan Xu, Jingdong Institute of Systems and Networks College of Computer Science Nankai University China University of Oregon Department of Computer Science United States School of Computer Science and Technology Tianjin University China

ISBN: (纸本)9798350399738

In this paper, we advocate CPN-FedSL, a novel and flexible Federated Split Learning (FedSL) framework over Computing Power Network (CPN). We build a dedicated model to capture the basic settings and learning characteristics (e.g., training flow, latency and convergence). Based on this model, we introduce Resource Usage Effectiveness (RUE), a novel performance metric integrating training utility with system cost, and formulate a multivariate scheduling problem that maximizes RUE by comprehensively taking client admission, model partition, server selection, routing and bandwidth allocation into account (i.e., mixed-integer fractional programming). We design Refinery, an efficient approach that first linearizes the fractional objective and non-convex constraints, and then solves the transformed problem via a greedy based rounding algorithm in multiple iterations. Extensive evaluations corroborate that CPN-FedSL is superior to the standard and state-of-the-art learning frameworks (e.g., FedAvg and SplitFed), and besides Refinery is lightweight and significantly outperforms its variants and de facto heuristic methods under a variety of settings. © 2023 IEEE.

关键词： Integer programming

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Reliability-aware swarm based multi-objective optimization for controller placement in distributed SDN architecture

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Digital communications and networks 2024年第5期10卷 1245-1257页

作者： Abeer A.Z.Ibrahim Fazirulhisyam Hashim Aduwati Sali Nor K.Noordin Keivan Navaie Saber M.E.Fadul Department of Computer and Communication Systems Engineering Faculty of EngineeringUniversiti Putra MalaysiaSerdang43400Malaysia Wireless and Photonics Networks Research Centre(WiPNET) Faculty of EngineeringUniversiti Putra MalaysiaSerdang43400Malaysia Department of Communication and Computer Engineering College of Engineering and Medical SciencesKhartoum11111Sudan School of Computing and Communications Lancaster UniversityLancasterLA14YWUnited Kingdom Department of Electrical and Electronics Engineering Faculty of EngineeringUniversiti Putra MalaysiaSerdang43400Malaysia

The deployment of distributed multi-controllers for Software-Defined Networking(SDN)architecture is an emerging solution to improve network scalability and ***,the network control failure affects the dynamic resource allocation in distributed networks resulting in network disruption and low ***,we consider the control plane fault tolerance for cost-effective and accurate controller location models during control plane *** fault-tolerance strategy has been applied to distributed SDN control architecture,which allows each switch to migrate to next controller to enhance network *** this paper,the Reliable and Dynamic Mapping-based Controller Placement(RDMCP)problem in distributed architecture is framed as an optimization problem to improve the system reliability,quality,and *** considering the bound constraints,a heuristic state-of-the-art Controller Placement Problem(CPP)algorithm is used to address the optimal assignment and reassignment of switches to nearby controllers other than their regular *** algorithm identifies the optimal controller location,minimum number of controllers,and the expected assignment costs after failure at the lowest effective cost.A metaheuristic Particle Swarm Optimization(PSO)algorithm was combined with RDMCP to form a hybrid approach that improves objective function optimization in terms of reliability and *** effectiveness of our hybrid RDMCP-PSO was then evaluated using extensive experiments and compared with other baseline *** findings demonstrate that the proposed hybrid technique significantly increases the network performance regarding the controller number and load balancing of the standalone heuristic CPP algorithm.

关键词： Software defined networking Dynamic mapping Particle swarm optimization Reliability Multi-objective optimization Evolutionary

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5G Channel Estimation Kernels on RISC-V Vector Digital Signal Processors

5G Channel Estimation Kernels on RISC-V Vector Digital Signa...

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2024 International Conference on Microelectronics, ICM 2024

作者： Acevedo, Javier Fitzek, Frank H. P. Seeling, Patrick Deutsche Telekom Chair of Communication Networks TU Dresden Germany Germany Central Michigan University Department of Computer Science United States

ISBN: (纸本)9798350379396

Each generation of the wireless communication networks has contiguous demands for higher computing capabilities and lower the power consumption in the integrated circuitry. Thus, the optimization of the base-band signal processing kernels and the hardware architectures plays a pivotal role in the performance, latency, and energy efficiency of the Physical Layer (PHY). Channel Estimation (CE), a computationally intensive task with hard real-time requirements, involves significant amounts of matrix multiplications and inversions, when data flows between transmitter (TX) and receiver (RX). In this work, we present a software implementation of CE for the 5th Generation Cellular networks (5G) New Radio (NR) in the Reduced Instruction Set computer (RISC)-V architecture, showcasing how its vector processing capabilities can satisfy the throughput and delay requirements of 5G use cases. Specifically, we ported two CE kernels, the Least-Squares (LSE) and Minimum Mean Square Error (MMSE), onto a state-of-the-art single core vector processor. By exploiting data vectorization, the ARA core computes matrix arithmetic operations on multiple data sets simultaneously. Our single vector core serial optimization demonstrates that with a 16× 16 matrix window size and 16 vector lanes, the application's performance achieves a speedup of 78.27 when computing the LSE algorithm. When the number of operations per cycle are compared, the vector processor outperforms its scalar counterpart by 181.91 for the MMSE kernel. Our implementation orders the nested loops in a latency-reducing fashion to compute matrix arithmetic operations, resulting in LSE and MMSE estimator computing times of 8.44 μiota s and 12.31 μs, respectively. © 2024 IEEE.

关键词： Mean square error

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