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IWM-LSTM encoder for abstractive text summarization

Multimedia Tools and Applications 2025年第9期84卷 5883-5904页

作者： Gangundi, Ravindra Sridhar, Rajeswari Department of Computer Science and Engineering National Institute of Technology Tamilnadu Tiruchirappalli620015 India

Sequence-to-sequence models are fundamental building blocks for generating abstractive text summaries, which can produce precise and coherent summaries. Recently proposed, different text summarization models aimed to enhance summarization performance through the use of copying mechanisms, reinforcement learning, and multiple-level encoders. However, there has been limited research on improving the summarization output by modifying the structure of the long short-term memory (LSTM) cell. We introduced an improved version of LSTM called improved working memory LSTM (IWM-LSTM). IWM-LSTM removes the output gate and enhances the input and forget gates by incorporating cell state information into these gates. In our sequence-to-sequence model for text summarization, we replaced the LSTM encoder with a bi-directional IWM-LSTM, resulting in better summaries with minimal training time and less computational intensiveness. Additionally, we utilized Bidirectional encoder representations from transformers (BERT) embeddings to enhance the rouge score. The CNN/DailyMail dataset is used to train and test the model performance. The proposed model achieves better Recall-oriented understudy for gisting evaluation (ROUGE) scores than state-of-the-art models. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Embeddings

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Review on Meta-heuristic Algorithm-Based Priority-Aware Computation Offloading in Edge Computing System

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Journal of The Institution of Engineers (India): Series B 2025年 1-26页

作者： Pilli, Neelima Mohapatra, Debasis Reddy, Shiva Shankar Department of Computer Science and Engineering Biju Patnaik University of Technology Odisha Rourkela India Department of Computer Science and Engineering Parala Maharaja Engineering College Odisha Brahmpur India Department of Computer Science and Engineering Sagi Rama Krishnam Raju Engineering College Andhra Pradesh Bhimavaram India

Advancements in smart applications highlight the need for increased processing and storage capacity at Smart Devices (SDs). To tackle this, Edge computing (EC) is enabled to offload SD workloads to distant edge servers. So, a variety of offloading strategies are offered in previous research to enhance the efficiency of SDs by offloading their workloads to neighboring cloudlets or distant cloud computing resources. Consequently, optimization techniques are crucial for scheduling computation offloading tasks in (ED) networks. To the greatest extent of current understanding, an extensive analysis of meta-heuristic-based computation offloading (CO) techniques in the EC has not been systematically surveyed. This systematic survey creates an inventory of CO in EC and further summarizes the most advanced recently available EC technologies. In this survey, three types of optimization techniques are examined: Lyapunov, Meta-heuristic, and Convex. The objective functions, application domains, offloading methods, evaluation techniques, merits, and demerits of the existing algorithms are summarized. The proposed survey undergoes four main CO processes: task scheduling, priority-aware task scheduling, load balancing, and edge server selection. In addition, this research provides a comparative analysis of the offloading techniques. Subsequently, unresolved problems and upcoming unfocused or inadequately covered research tasks are explored. © The Institution of Engineers (India) 2025.

关键词： Computation offloading

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Attention enabled viewport selection with graph convolution for omnidirectional visual quality assessment

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Multimedia Tools and Applications 2025年第14期84卷 12925-12948页

作者： C, Nandhini M, Brindha Department of Computer Science and Engineering National Institute of Technology TamilNadu Tiruchirappalli620015 India

Omnidirectional images provide an immersive viewing experience in a Virtual Reality (VR) environment, surpassing the limitations of traditional 2D media beyond the conventional screen. This VR technology allows users to interact with visual information in an exciting and engaging manner. However, the storage and transmission requirements for 360-degree panoramic images are substantial, leading to the establishment of compression frameworks. Unfortunately, these frameworks introduce projection distortion and compression artifacts. With the rapid growth of VR applications, it becomes crucial to investigate the quality of the perceptible omnidirectional experience and evaluate the extent of visual degradation caused by compression. In this regard, viewport plays a significant role in omnidirectional image quality assessment (OIQA), as it directly affects the user’s perceived quality and overall viewing experience. Extracting viewports compatible with users viewing behavior plays a crucial role in OIQA. Different users may focus on different regions, and the model’s performance may be sensitive to the chosen viewport extraction strategy. Improper selection of viewports could lead to biased quality predictions. Instead of assessing the entire image, attention can be directed to areas that are more importance to the overall quality. Feature extraction is vital in OIQA as it plays a significant role in representing image content that aligns with human perception. Taking this into consideration, the proposed ATtention enabled VIewport Selection (ATVIS-OIQA) employs attention based view port selection with Vision Transformers(ViT) for feature extraction. Furthermore, the spatial relationship between the viewports is established using graph convolution, enabling intuitive prediction of the objective visual quality of omnidirectional images. The effectiveness of the proposed model is demonstrated by achieving state-of-the-art results on publicly available benchmark datasets, n

关键词： Convolution

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A two-stage method with twin autoencoders for the degradation trajectories prediction of lithium-ion batteries

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Journal of Energy Chemistry 2025年第4期103卷 759-772页

作者： Lei Cai Jing Yan Haiyan Jin Jinhao Meng Jichang Peng Bin Wang Wei Liang Remus Teodorescu School of Computer Science and Engineering Xi'an University of Technology School of Electrical Engineering Xi'an Jiaotong University Smart Grid Research Institute Nanjing Institute of Technology Department of Energy Aalborg University

To predict the lithium-ion(Li-ion) battery degradation trajectory in the early phase,arranging the maintenance of battery energy storage systems is of great ***,under different operation conditions,Li-ion batteries present distinct degradation patterns,and it is challenging to capture negligible capacity fade in early *** the data-driven method showing promising performance,insufficient data is still a big issue since the ageing experiments on the batteries are too slow and *** this study,we proposed twin autoencoders integrated into a two-stage method to predict the early cycles' degradation *** two-stage method can properly predict the degradation from course to *** twin autoencoders serve as a feature extractor and a synthetic data generator,***,a learning procedure based on the long-short term memory(LSTM) network is designed to hybridize the learning process between the real and synthetic *** performance of the proposed method is verified on three datasets,and the experimental results show that the proposed method can achieve accurate predictions compared to its competitors.

关键词： Autoencoder Battery degradation trajectory Early prediction Synthetic data generation

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An efficient secure channel establishment through lightweight key distribution in e-health communication systems

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Multimedia Tools and Applications 2025年第11期84卷 9111-9131页

作者： Ebrahimi, Iman Nikooghadam, Morteza Department of Computer Engineering and Information Technology Imam Reza International University Mashhad Iran

E-healthcare has significantly improved healthcare services and overall health by utilizing digital technologies such as the internet, computers, and mobile devices. However, secure communication channels play a critical role in ensuring the safety and confidentiality of patients' information. Consequently, several user authentication and key agreement protocols have been developed to address this issue. Although numerous proposed protocols are robust against security attacks, researchers have reported vulnerabilities. In this study, we evaluate one such protocol and identify vulnerabilities to insider attacks, server impersonation attacks, and replay attacks. To address these vulnerabilities, we propose a two-factor mutual authentication and key agreement scheme that offers a secure, efficient, and effective solution with low computation cost, enhanced security, and superior performance against diverse security attacks. Our proposed technique guarantees the integrity, confidentiality, and availability of patient information while ensuring the authenticity of users during communication. We conducted thorough comparisons focusing on the practical implementation of ARM microcontrollers to assess computation efficiency. The total estimated execution time of our proposed scheme for registration and authentication phases is approximately 975.8 µs, which is the shortest among all related works. This finding illustrates that our proposed scheme is well-suited for applications in e-health, telemedicine, and healthcare systems. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Authentication

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Semantic Segmentation of Lumbar Vertebrae Using Meijering U-Net(MU-Net)on Spine Magnetic Resonance Images

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computer Modeling in engineering & Sciences 2025年第1期142卷 733-757页

作者： Lakshmi S V V Shiloah Elizabeth Darmanayagam Sunil Retmin Raj Cyril Department of Computer Science and Engineering College of Engineering GuindyAnna UniversityChennai600025India Department of Information Technology Madras Institute of TechnologyAnna UniversityChrompetChennai600044India

Lower back pain is one of the most common medical problems in the world and it is experienced by a huge percentage of people *** to its ability to produce a detailed view of the soft tissues,including the spinal cord,nerves,intervertebral discs,and vertebrae,Magnetic Resonance Imaging is thought to be the most effective method for imaging the *** semantic segmentation of vertebrae plays a major role in the diagnostic process of lumbar *** is difficult to semantically partition the vertebrae in Magnetic Resonance Images from the surrounding variety of tissues,including muscles,ligaments,and intervertebral discs.U-Net is a powerful deep-learning architecture to handle the challenges of medical image analysis tasks and achieves high segmentation *** work proposes a modified U-Net architecture namely MU-Net,consisting of the Meijering convolutional layer that incorporates the Meijering filter to perform the semantic segmentation of lumbar vertebrae L1 to L5 and sacral vertebra ***-colour mask images were generated and used as ground truth for training the *** work has been carried out on 1312 images expanded from T1-weighted mid-sagittal MRI images of 515 patients in the Lumbar Spine MRI Dataset publicly available from Mendeley *** proposed MU-Net model for the semantic segmentation of the lumbar vertebrae gives better performance with 98.79%of pixel accuracy(PA),98.66%of dice similarity coefficient(DSC),97.36%of Jaccard coefficient,and 92.55%mean Intersection over Union(mean IoU)metrics using the mentioned dataset.

关键词： computer aided diagnosis(CAD) magnetic resonance imaging(MRI) semantic segmentation lumbar vertebrae deep learning U-Net model

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Towards Understanding the Impact of Participant and its Wearable Devices in Federated Learning

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IEEE Transactions on Mobile Computing 2025年第6期24卷 5003-5015页

作者： Mishra, Rahul Gupta, Hari Prabhat Department of Computer Science and Engineering Institute of Technology Patna Patna India Varanasi India

The popularity of wearable smart devices has increased due to their seamless monitoring of vital signs during daily activities. Federated learning leverages these devices along with participants’ smartphones to fine-tune pre-trained models. Moreover, calibrating the differences between wearables and smartphones in terms of sampling rates, orientations, activity correlation, battery power, and other factors is challenging. Thus, the paper introduces a participant and wearable selection cross-device federated learning approach. It leverages criteria such as the activity wearable(s) relationship, data quality, battery life, sampling rate, and so on to perform the wearable selection. The server evaluates and estimates the utility of each participant and selects those with higher utility in each communication round. We then figure out the optimal weighted contribution of each participant to perform robust aggregation. We also use knowledge distillation techniques to develop a high-performing and lightweight wearable model. Finally, we conduct simulation and real-world experiments on existing datasets and compare our approach with state-of-the-art. The result shows an improvement of 3−4% in accuracy via fine-tuning from selected wearable data. © 2002-2012 IEEE.

关键词： Federated learning

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Spatial attention-based hybrid VGG-SVM and VGG-RF frameworks for improved cotton leaf disease detection

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Neural Computing and Applications 2025年第14期37卷 8309-8329页

作者： Pandiyaraju, V. Anusha, B. Senthil Kumar, A.M. Jaspin, K. Venkatraman, Shravan Kannan, A. School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Chennai India Department of Computer Science and Engineering Marian Engineering College Trivandram Kerala Trivandrum India Department of Computer Science and Engineering St. Joseph’s Institute of Technology Tamil Nadu Chennai India Department of Information Science and Technology College of Engineering Guindy Anna University Tamil Nadu Chennai India

The agriculture industry's production and food quality have been impacted by plant leaf diseases in recent years. Hence, it is vital to have a system that can automatically identify and diagnose diseases at an initial stage for enhancing the quality of agricultural output and also for stopping the overall plant extinction. Most state-of-the-art methods do not provide appropriate accuracy of identification when the similarity among different diseases is higher and the input leaf images have complex background. In this work, a transfer learning-based automated crop disease recognition system is proposed to handle this problem. The cotton leaf disease detection framework being proposed here includes two key stages: (1) the feature extraction stage, where the convolutional neural network (CNN) model from the visual geometry group (VGG-16) is employed to obtain the deep features from cotton leaf images with complex background. (2) Cotton leaf disease detection stage: Here, the extracted optimal features are input to the machine learning classifiers, namely random forest (RF) and nonlinear support vector machine (SVM), for the initial classification of the diseases. For providing an optimal cotton crop disease detection performance, Kaggle’s four-class cotton disease dataset with complex background is chosen in this work. For the final decision making and analysis, two hybrid models named spatial attention-based hybrid VGG-RF and hybrid VGG-SVM have been proposed in this work for enhancing the decision accuracy. These hybrid models offered an average accuracy of approximately 98.29% and 99.31%, respectively, which is higher than the accuracy provided by the related work. This proved that the proposed hybrid model, namely spatial attention-based VGG-SVM and VGG-RF models, with spatial attention outperformed the state-of-the-art classifiers. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Convolutional neural networks

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Farm-Level Smart Crop Recommendation Framework Using Machine Learning

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Annals of Data Science 2025年第1期12卷 117-140页

作者： Bhola, Amit Kumar, Prabhat Department of Computer Science and Engineering National Institute of Technology Patna Bihar Patna India

Agriculture is the primary source of food, fuel, and raw materials and is vital to any country’s economy. Farmers, the backbone of agriculture, primarily rely on instinct to determine what crops to plant in any given season. They are comfortable following customary farming practices and standards and are oblivious to the fact that crop yield is highly dependent on current environmental and soil conditions. Crop recommendations involve multifaceted factors such as weather, soil quality, crop production, market demand, and prices, making it crucial for farmers to make well-informed decisions. An improper or imprudent crop recommendation can affect them, their families, and the entire agricultural sector. Modern technologies like artificial intelligence, machine learning, and data science have emerged as efficient solutions to combat issues like declining crop production and lower profits. This research proposes a Smart Crop Recommendation framework that leverages machine learning to empower farmers to make informed decisions about optimal crop selection. The framework consists of two phases: crop filtration and yield prediction. Crops are filtered in the first phase using an artificial neural network based on local input parameters. The second phase estimates yield for filtered crops, considering the season, farm area, and location data. The final recommendation provides farmers with crops aimed at maximizing profit. The remarkable 99.10% accuracy of the framework is demonstrated through experimentation using artificial neural networks and the 0.99 R2 error metric for the random forest. The uniqueness of this framework lies in its distinctive focus on the farm level and its consideration of the challenges and various agricultural features that change over time. The experimental results affirm the effectiveness of the framework, and its lightweight nature enhances its practicality, making it an efficient real-time recommendation solution. © The Author(s), under exclusi

关键词： Crops

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Leveraging BERT, DistilBERT and TinyBERT for Rumor Detection

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IEEE Access 2025年 13卷 72918-72929页

作者： Qazi, Aijazahamed Goudar, R.H. Patil, Rudragoud Hukkeri, Geetabai S. Kulkarni, Dhanashree Angadi Institute of Technology and Management Department of Artificial Intelligence and Data science Belagavi591108 India Visvesvaraya Technological University Department of Computer Science and Engineering Belagavi590018 India KLS Gogte Institute of Technology Department of Computer Science and Engineering Belagavi590008 India Manipal Academy of Higher Education Manipal Institute of Technology Department of Computer Science and Engineering Bengaluru Karnataka Manipal576104 India Angadi Institute of Technology and Management Department of Computer Science and Engineering Belagavi591108 India

The rapid spread of false information on social media has become a major challenge in today's digital world. This has created a need for an effective rumor detection system that can identify and control the spread of false information in real-time. The proposed work introduces a rumor detection system by integrating transformer-based models such as BERT, DistilBERT, and TinyBERT with traditional Machine Learning (ML) techniques. The proposed method involves fine-tuning transformer models to recognize the contextual meaning of social media posts. The classifiers include Decision Tree (DT), Support Vector Machine (SVM), Random Forest (RF) and Naïve Bayes (NB), which help in categorizing content as either rumor or non-rumor based on the patterns. The proposed work evaluated BERT, DistilBERT, TinyBERT combined with ML models (SVM, DT, RF, NB) across PHEME dataset using 70:30, 60:40, and 80:20 splits. Overall, BERT+DT and TinyBERT+SVM provided significant results, with BERT+RF and DistilBERT+NB demonstrating better classification capabilities across various events and split ratios on the dataset. © 2013 IEEE.

关键词： Tweets

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