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Aspect-oriented extraction and sentiment analysis using optimized hybrid deep learning approaches

Multimedia Tools and Applications 2024年第41期83卷 88613-88644页

作者： Kotagiri, Srividya Sowjanya, A. Mary Anilkumar, B. Devi, N Lakshmi Computer Science and Engineering GMR Institute of Technology MR Nagar Andhra Pradesh Razam532127 India Andhra Pradesh Visakhapatnam530003 India Electronics and Communication Engineering GMR Institute of Technology MR Nagar Andhra Pradesh Razam532127 India

Aspect-oriented extraction involves the identification and extraction of specific aspects, features, or entities within a piece of text. Traditional methods often struggled with the complexity and variability of language, leading to the exploration of advanced deep learning approaches. In the realm of sentiment analysis, the conventional approaches often fall short when it comes to providing a nuanced understanding of sentiments expressed in textual data. Traditional sentiment analysis models often overlook the specific aspects or entities within the text that contribute to the overall sentiment. This limitation poses a significant challenge for businesses and organizations aiming to gain detailed insights into customer opinions, product reviews, and other forms of user-generated *** this research, we propose an innovative approach for aspect-oriented extraction and sentiment analysis leveraging optimized hybrid deep learning techniques. Our methodology integrates the powerful capabilities of deep learning models with the efficiency of Reptile Search Optimization. Furthermore, we introduce an advanced sentiment analysis framework employing the state-of-the-art Extreme Gradient Boosting Algorithm. The fusion of these techniques aims to enhance the precision and interpretability of aspect-oriented sentiment analysis. The proposed approach first utilizes deep learning architectures to extract and comprehend diverse aspects within textual data. Through the incorporation of Reptile Search Optimization, we optimize the learning process, ensuring adaptability and improved model generalization across various datasets. Subsequently, the sentiment analysis phase employs the robust Extreme Gradient Boosting Algorithm, known for its effectiveness in handling complex relationships and patterns within data. Our experiments, conducted on diverse datasets, demonstrate the superior performance of the proposed methodology in comparison to traditional approaches. The optimized

关键词： Extraction

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Load Balancing and Energy Efficient Routing in Software-Defined Networking 1st

Load Balancing and Energy Efficient Routing in Software-Defi...

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1st International Conference on Advanced Computing, Machine Learning, Robotics and Internet Technologies, AMRIT 2023

作者： Ramkumar, M.P. Rani, J. Lece Elizabeth Jeyarohini, R. Selvan, G. S. R. Emil Konar, Rahul Shiva Department of Computer Science and Engineering Thiagarajar College of Engineering Madurai India Department of Electronics and Communication Engineering PSNA College of Engineering and Technology Dindigul India

ISBN: (纸本)9783031472237

Software-defined networking (SDN), a newly developed architecture that alters the route (paths) of traffic packets, is a flexible and logically centralized control plane that provides a reduction in the network’s energy usage. SDN is designed in such a way that the switches and links can handle a large amount of traffic. Their energy use is not proportional to traffic. Due to the increase in network communication, SDN devices demand much more load and energy. The SDN controller has to be modeled efficiently. The system model of the controller is proposed which efficiently distributes the load. This model helps in traffic routing with minimal energy consumption for networking devices. In addition to the above, it balances the load based on the heterogeneous traffic distribution. The proposed method optimizes the energy consumed by the networking devices by means of using the few active switches, minimizing the number of device state transitions, and handling the volume of traffic. Load balancing is performed for multiple controllers. The switch migration technique is used for migrating the traffic from one controller to the other. Energy-efficient routing is done by the proposed algorithm with reduced device state transition. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Software defined networking

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Insights into Ocular Disease Recognition: A Machine Learning Approach 3

Insights into Ocular Disease Recognition: A Machine Learning...

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3rd International Conference on Applied Artificial Intelligence and Computing, ICAAIC 2024

作者： Paul, Sosmita Bhatt, Geet Dey, Aditya Sarvajanik College of Engineering and Technology Be in Electronics and Communication Engineering Gujarat Surat India Be in Computer Engineering L D College of Engineering Gujarat Ahmedabad India

ISBN: (纸本)9798350375190

This study reviews the potential benefits of machine learning techniques in diagnosing ocular disorders, aiming to address the limitations of traditional diagnostic methods. Various Machine Learning (ML) algorithms, including Support Vector Machine, Decision Tree, K-Nearest Neighbor, Random Forest, Naïve Bayes, and Extra Trees, are compared to determine the effectiveness of each algorithm in identifying eye diseases. Through analysis and testing, the study concludes that Extra Trees (ET) has achieved the highest accuracy rate of 97%. This finding suggests that ET could be the most suitable option for enhancing the accuracy and efficiency of eye disease detection systems. Overall, these research discoveries contribute to the ongoing efforts to improve diagnostic tools for ocular health, highlighting the potential of Machine Learning (ML) in transforming healthcare. © 2024 IEEE.

关键词： Decision trees

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A Multicriteria Aware Multipath Routing Method to Increase the QoS for Future 6G Networks 13

A Multicriteria Aware Multipath Routing Method to Increase t...

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13th IEEE International Conference on Communication Systems and Network Technologies, CSNT 2024

作者： Tilwari, Valmik Sharma, Debashree Solanki, Sourabh Chakraborty, Nilotpal Maduranga, M.W.P. Indian Institute of Information Technology Department of Electronics & Communication Engineering Guwahati India National Institute of Technology Department of Electronics & Communication Engineering Warangal India Indian Institute of Information Technology Department of Computer Science Engineering Guwahati India General Sir Jhon Kotelawala Defence University Department of Computer Engineering Colombo Sri Lanka

ISBN: (纸本)9798350305463

The optimal solution of creating a communications network in isolated places is still a challenging task. Establishing end-to-end connectivity via mobile ad-hoc networks (MANETs) is a better solution for the above issue. However, the limitation of the mobility of nodes and battery resources between the overlay and mobile ad-hoc network protocols is still a challenge for trustable wireless connections. Therefore, this paper presented a routing scheme named Mobility and Energy consumption aware multipath routing (MEMR) in mobile ad-hoc networks. The proposed routing scheme provides reliable communication with stable route and high lifetime routes to support efficient data transmission in the network. For this purpose, it uses a multicriteria decision-making approach to select the intermediate nodes with the highest energy and lowest mobility for selecting the optimal route. The obtained results indicate that the proposed routing method can significantly improve the overall network performance and deliver packets with acceptable throughput. At the same time, it reduces the energy consumption of the nodes compared with the conventional MP-OLSRv2 routing protocol. © 2024 IEEE.

关键词： Energy utilization

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ViF-SD2E: a robust weakly-supervised framework for neural decoding

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Neural Computing and Applications 2025年第9期37卷 6645-6661页

作者： Feng, Jingyi Luo, Yong Song, Shuang Hu, Han National Engineering Research Center for Multimedia Software School of Computer Science Wuhan University Wuhan430072 China School of Information and Electronics Beijing Institute of Technology Beijing100081 China

Neural decoding plays a vital role in the interaction between the brain and the outside world. Our task in this paper is to decode the movement track of a finger directly based on the neural data. Existing neural decoding solutions primarily perform some preprocessing operations on neural data before feeding them into existing models (such as LSTM) for decoding. However, these solutions either are prone to overfitting or cannot well exploit the spatial and temporal information. In our previous observations, there is a symmetrical phenomenon between the unsupervised decoded trajectory and the ground truth trajectory within the activity space. This precisely motivates us to propose (or derive) a robust weakly-supervised framework (or model structure), called ViF-SD2E, for neural decoding. In particular, it consists of a space-division (SD) module and an exploration–exploitation (2E) strategy, to effectively exploit both the spatial information of the outside world and the temporal information of neural activity, where the SD2E output is analogized with the weak 0/1 vision feedback (ViF) label for training. Extensive experiments demonstrate the effectiveness of our method, which can sometimes be comparable to supervised counterparts. Therefore, we redirect our attention to the information (hidden in data) ViF-SD2E conveys to us. In other words, we believe that the advantage of ViF-SD2E lies in the fact that its processing steps are objectively determined by the inherent attributes (i.e., symmetry) of the neural data, or rather, the model structure is fixed. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Space division multiple access

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Performance Enhancement of High-gain STDA Antennas with Reflector for 4G LTE and Sub-6 GHz 5G Applications: Design, Measurement, and Analysis

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Progress In Electromagnetics Research C 2024年 139卷 87-94页

作者： Wasim, Mohd Khera, Shelej Islam, Tanvir Malik, Praveen Kumar Asha, Sivaji Das, Sudipta School of Electronics and Electrical Engineering Lovely Professional University Phagwara144411 India Department of Electrical and Computer Engineering University of Houston HoustonTX77204 United States Saveetha Engineering College Saveetha Nagar Thandalam Chennai602105 India Department of Electronics and Communication Engineering IMPS College of Engineering and Technology West Bengal Malda732103 India

The paper focuses on the design, measurement, and performance analysis of a high-gain cross-orthogonal series fed two dipole antenna (STDA) arrays with side-wall reflectors. The antenna is specifically designed for 4G Long Term Evolution (LTE) and sub-6 GHz 5G band applications. The designed antenna is capable of operating at multiple frequencies aiming to support 4G LTE and the sub-6 GHz 5G application bands. To improve the radiation characteristics and prevent coupling effects in the presence of side-wall reflectors, parasitic strip pair directors are included in the antenna design. Furthermore, the performance of the designed STDA is evaluated by forming different array configurations, such as 2 × 1, 2 × 2, and 2 × 3 arrays. The various array configurations are proposed to investigate the effect of the projected array arrangements on the radiation pattern, impedance bandwidth, and gain characteristics. The results of the measurements show that the radiation characteristics of the antenna have been improved significantly. The proposed antenna operates at six distinct frequencies for S11 ≤ 10 dB. The operating frequencies at 1.8, 2.35, and 2.6 GHz can be utilized for LTE and 3.2, 4.2, and 5.2 GHz which can support sub-6 GHz 5G bands. The antenna is characterized by its compact size, measuring around 89 mm × 71 mm, while still achieving high gain of 12.3 dB for single STDA element with parasites and with reflector. These results emphasize the importance of the proposed design, which incorporates parasitic strip pair directors and side-wall reflectors. This design methodology plays a crucial role in enhancing the performance of the prescribed STDA array for both 4G LTE and sub-6 GHz 5G applications. © 2024, Electromagnetics Academy. All rights reserved.

关键词： Dipole antennas

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An Efficient Machine Learning-Based Power Management System for Smart Grids Using Renewable Energy Resources 2

An Efficient Machine Learning-Based Power Management System ...

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2nd IEEE International Conference on Networks, Multimedia and Information technology, NMITCON 2024

作者： Kavya, B.M. Mallikarjunaswamy, S. Sharmila, N. Shilpa, M. Komala, M. Shivaji, R. Balachandra, P. Sheela, S. Pavithra, G.S. Ks Institute of Technology Dept of Electronics and Communication Engineering Bengaluru India Jss Academy of Technical Eduction Dept of Electronics and Communication Engineering Bengaluru India Jss Science and Technology University Dept of Electronics and Electrical Engineering Mysuru India Bengaluru Institute of Technology Dept of Information Science and Engineering Bengaluru India Sjb Institute of Technology Dept of Electronics and Communication Engineering Bengaluru India Jagadambha college of Engineering & Technology Dept Electronics and Telecommunication Engineering India Sjb Institute of Technology Dept of Mathematics Bengaluru India Global Academy of Technology Dept of Computer Sceinec and Engineering Bengaluru India Jss Academy of Technical Eduction Dept of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350372892

In the dynamic landscape of energy systems, integrating renewable energy resources into smart grids presents substantial challenges, notably due to the inherent variability of wind and solar power. This study unveils an advanced power management system for smart grids that harnesses machine learning algorithms (ApmSGML) to optimize energy distribution and storage, ensuring stability, efficiency, and environmental sustainability. This approach is distinguished from conventional methods such as Fixed Threshold Control and Rule-Based Scheduling, which often struggle to adapt to the unpredictable nature of renewable energy sources. By leveraging predictive models, the proposed system anticipates energy production and consumption patterns, facilitating proactive adjustments to maintain supply and demand equilibrium. Additionally, A comparison of the ApmSGML's performance against traditional power management techniques like Deterministic Load Forecasting and Static Energy Storage Management, underscoring the adaptive and continuous learning advantages offered by machine learning through techniques like deep learning and reinforcement learning. Our findings highlight significant advancements in energy efficiency and cost reduction, demonstrating the transformative potential of integrating machine learning into the energy sector. © 2024 IEEE.

关键词： Internet of things (IoT) Machine learning power management Renewable energy smart grid

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Energy Efficient Transmitter Creation by Consuming Free Energy in Molecular Communication

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IEEE Transactions on Molecular, Biological, and Multi-Scale Communications 2025年第2期11卷 292-303页

作者： Jing, Dongliang Li, Linjuan Cheng, Zhen Lin, Lin Eckford, Andrew W. Northwest A and F University College of Mechanical and Electronic Engineering Yangling China Ministry of Agriculture and Rural Affairs Key Laboratory of Agricultural Internet of Things Yangling China Zhejiang University of Technology School of Computer Science and Technology Hangzhou310023 China Tongji University College of Electronics and Information Engineering Shanghai China York University Department of Electrical Engineering and Computer Science TorontoON Canada

Information molecules play a crucial role in molecular communication (MC), acting as carriers for information transfer. A common approach to get information molecules in MC involves harvesting them from the environment;however, the harvested molecules are often a mixture of various environmental molecules, and the initial concentration ratios in the reservoirs are identical, which hampers high-fidelity transmission techniques such as molecular shift keying (MoSK). This paper presents a transmitter design that harvests molecules from the surrounding environment and stores them in two reservoirs. To separate the mixed molecules, energy is consumed to transfer them between reservoirs. Given limited energy resources, this work explores energy-efficient strategies to optimize transmitter performance. Through theoretical analysis and simulations, we investigate different methods for moving molecules between reservoirs. The results demonstrate that transferring higher initial concentration molecules enhances transmitter performance, while using fewer molecules per transfer further improves efficiency. These findings provide valuable insights for optimizing MC systems through energy-efficient molecule transfer techniques. © 2015 IEEE.

关键词： Transmitters

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A Semi-Supervised Deep Learning Approach for Intrusion Detection and Classification for the Internet of Things

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Biomedical Materials and Devices 2025年 1-17页

作者： Uthradevi, G. Thiruvasagam, P. Mythili, S. Manoj, S. Oswalt Department of Electronics and Communication Engineering Chennai Institute of Technology Tamil Nadu Kundrathur Chennai 600069 India Department of Electrical and Electronics Engineering Chennai Institute of Technology Tamil Nadu Kundrathur Chennai 600069 India Department of Biomedical Engineering PSNA College of Engineering and Technology Tamilnadu Dindigul 624622 India Department of Computer Science and Engineering Sri Krishna College of Engineering and Technology Tamil Nadu Kuniyamuthur 641008 India

Intrusion Detection Systems (IDS) are essential for protecting networks from cyber threats and ensuring the security of critical infrastructures. This paper introduces an innovative hybrid model that combines Deep Belief Networks (DBNs), Principal Component Analysis (PCA), and Support Vector Machines (SVM) with adversarial learning to enhance the detection of both known and emerging cyber threats. The proposed model achieves a strong balance of accuracy, efficiency, and scalability, making it highly effective for real-world applications. By incorporating adversarial learning, the system can detect zero-day exploits—previously unseen attacks—and adapt to evolving threats. This is achieved by training the model on adversarial samples, which improves its resilience to variations in attack patterns. SVM further enhances the model’s ability to classify known and unknown threats with high precision. The model was rigorously tested on two benchmark datasets, NSL-KDD and CICIDS2017, achieving outstanding results. It demonstrated a high accuracy rate of 99.73% on NSL-KDD and a low false positive rate of 0.55%, while on CICIDS2017, it achieved a false positive rate of 0.73%. The model also outperformed existing approaches in precision, recall, and F1-score, particularly in detecting complex attack types such as R2L (Remote-to-Local), U2R (User-to-Root), and Botnet attacks. Additionally, PCA reduced the feature space by 40%, significantly lowering the model’s inference time to just 9.0 ms per sample. This makes the model highly suitable for real-time intrusion detection in high-traffic network environments, where speed and efficiency are critical. Overall, this hybrid model strengthens cybersecurity defenses and contributes to building resilient infrastructures. By effectively detecting and mitigating cyber threats, it supports the creation of safer, fairer, and more inclusive digital communities. The integration of adversarial learning and dimensionality reduction ensures rob

关键词： Adversarial learning Cyber security Deep belief networks Dimensionality reduction Feature extraction IDS Networks PCA SVM

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Indoor 3D REM Design using Ordinary Kriging Interpolation

Indoor 3D REM Design using Ordinary Kriging Interpolation

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2024 IEEE International Conference on Communication, Computing and Signal Processing, IICCCS 2024

作者： Maiti, Pradipta Banerjee, Sudipta Dhanbad Department of Electronics Engineering Dhanbad India Symbiosis Institute of Technology Pune Department of Computer Science and Engineering Pune India

ISBN: (纸本)9798350390759

This study examines three-dimensional (3D) and two-dimensional (2D) interpolation approaches for indoor 3D radio environment map (REM), on received signal strength (RSS) data of ultrahigh frequency (UHF) active television (TV) channel, to explore indoor TV white space (TVWS) volumes. It could serve as a valuable resource for cognitive radio operations during unlicensed secondary access indoors. A significant portion of data traffic originates indoors. Altitude considerations or realistic view of indoor radio environment map could improve the detection of TVWS volumes, thereby boosting channel data rates and throughput, which are crucial in secondary access. The measured 3D RSS dataset assisted spatial interpolation, exponential and spherical semivariogram (SV) models based 3D and 2D ordinary kriging (OK), results evaluation has been done on two metrics - computation time (CT) and root mean square error (RMSE). Performance comparison of the spherical and exponential SV model for 3D and 2D OK, reveals that the spherical model outperforms the exponential model to a moderate extent for 3D-OK, while the exponential model marginally outdoes the spherical model in 2D-OK. Study finds that 3D-OK yields lower RMSE values compared to 2D-OK for spherical and exponential SV. The analysis indicates that 3D-OK achieves 17.41% and 13.98% higher accuracy using the spherical and exponential SV than 2D-OK. The CT of 3D-OK is 1.09 times faster than the 2D-OK. The best-tracked TVWS volume is 18200m3, 7.624% of the real building volume. The findings suggest considerable likelihood of accessing TVWS based on the RSS status of 3D indoor active TV channels. © 2024 IEEE.

关键词： Spheres

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