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Informatica (Slovenia) 2024年第12期48卷 41-54页

作者： Pal, Chandrajit Das, Samikshan Akuli, Amitava Adhikari, Sudip Kumar Dey, Aniruddha Department of Electrical Engineering IIT Hyderabad India Capgemini India Centre for Development of Advanced Computing Kolkata India Department of Computer Science & Engineering CGEC Cooch Behar India Department of Computer Science & Engineering MSIT Kolkata India

The process of cocoa hybridization produces new types that have unique chemical properties impacting the manufacturing of chocolate yet are resistant to a number of plant illnesses. Image analysis is a valuable tool for visually differentiating cocoa beans where deep neural networks (DNN) play a pivotal role in implementing them. In this manuscript, we compare machine learning and deep learning models because it takes into consideration multiple images covering a wide range of agricultural products. Specifically, we extract features from images using a series of image processing techniques, following which we use both traditional machine learning methods (KNN, Decision tree, SVM, and Random Forest) and Convolutional Neural Networks (proposed Cocoa-Net and RESNET 50) to classify the cocoa beans into four categories: large, medium, small, and rejected. Since each methodology offers strong classification performance and has potential for use in the classification of food, they were all chosen. To test these methods, a dataset including 200 samples of fragmented images was utilized. Studies that compare various similar approaches are also carried out. Two optimization techniques: Univariate Selection and Feature Importance have been leveraged to optimize the retrieved features before the learning models are trained. The Adam optimizer is used to optimize the proposed Cocoa-Net model. K-fold cross validation is utilized to assess trained models, and mean cross validation scores are then computed for performance analysis. The empirical result shows that, the proposed Cocoa-Net model predicts with the highest classification accuracy score of 0.85. © 2024 Slovene Society Informatika. All rights reserved.

关键词： cocoa beans Cocoa-Net decision tree KNN random Forest SVM

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Bidirectional Photoelectric Response of Perovskite Oxide-Based Synaptic ReRAM Device

IEEE Electron Device Letters

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IEEE Electron Device Letters 2024年第3期45卷 384-387页

作者： Shrivastava, Saransh Limantoro, Stephen Ekaputra Juliano, Hans Tseng, Tseung-Yuen Hsinchu30010 Taiwan Department of Electrical Engineering and Computer Science Hsinchu30010 Taiwan

Photo sensing capability of an artificial synaptic device make it more valuable for brain-inspired computing systems, which can conquer the von Neumann bottleneck. In this letter, Ba0.7 Sr0.3 TiO3 (BST) based single layered resistive random access memory (ReRAM) device is proposed with its four different photoelectric applications. First, bidirectional photoelectric response (BPR) is achieved via 405 and 633 nm wavelengths stimulation. Second, this novel BPR contributes to the emulation of light potentiation, and light depression behaviors. Third, an important neurological behavior 'experience dependent plasticity (EDP)' is mimicked by frequency modulation of 405 nm light pulse. Fourth, ultra-high (>200%) indices of post-tetanic potentiation and depression (PTP/PTD) are achieved by amplifying the device conductance as a result of two different pulse train stimulations. A convolutional neural network is designed to classify Zalando's article images with a test accuracy of 87.7%. These results provide an opportunity for the development of multi-wavelength dependent multifunctional artificial brain-inspired systems. © 1980-2012 IEEE.

关键词： Neural networks

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Virtual Power Plants for Grid Resilience: A Concise Overview of Research and Applications

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IEEE/CAA Journal of Automatica Sinica 2024年第2期11卷 329-343页

作者： Yijing Xie Yichen Zhang Wei-Jen Lee Zongli Lin Yacov A.Shamash IEEE Department of Electrical Engineering University of Texas at ArlingtonArlingtonTX 76019 USA Charles L.Brown Department of Electrical and Computer Engineering University of VirginiaCharlottesvilleVA 22904 USA Department of Electrical and Computer Engineering Stony Brook UniversityStony BrookNY 11794 USA

The power grid is undergoing a transformation from synchronous generators(SGs) toward inverter-based resources(IBRs). The stochasticity, asynchronicity, and limited-inertia characteristics of IBRs bring about challenges to grid resilience. Virtual power plants(VPPs) are emerging technologies to improve the grid resilience and advance the transformation. By judiciously aggregating geographically distributed energy resources(DERs) as individual electrical entities, VPPs can provide capacity and ancillary services to grid operations and participate in electricity wholesale markets. This paper aims to provide a concise overview of the concept and development of VPPs and the latest progresses in VPP operation, with the focus on VPP scheduling and control. Based on this overview, we identify a few potential challenges in VPP operation and discuss the opportunities of integrating the multi-agent system(MAS)-based strategy into the VPP operation to enhance its scalability, performance and resilience.

关键词： Climate change renewable energy resources resilience smart grids virtual power plants(VPPs)

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Informative task classification with concatenated embeddings using deep learning on crisisMMD

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International Journal of computers and Applications 2025年第2期47卷 123-140页

作者： Jain, Tarun Gopalani, Dinesh Kumar Meena, Yogesh Department of Computer Science and Engineering Manipal University Jaipur Jaipur India Department of Computer Science and Engineering Malaviya National Institute of Technology Jaipur India

Disastrous situations pose a formidable challenge, testing our resilience against nature's fury and the race against time to prevent the loss of human life. It is noted that in such situations that Microblogging platforms like Twitter(now X) have proven to be an invaluable resource for citizens to share the real-time updates in their locality. Building upon this premise, we are proposing a new hybrid robust approach that integrates transfer learning with selective features from Liwc and manually engineered features to achieve state-of-the-art results on the CrisisMMD dataset on the informative task. In our innovative approach, we harnessed the power of Deep learning combined with traditional ML practices to improve the model, we utilized OpenAI embeddings and minute details from the tweets like the sentiment, text polarity, Retweet address, links included etc. We employed a dense-dropout-based concatenate model for further processing. We established baselines of each technique individually as well as commonly used techniques like Tf-Idf, BoW, Glove embeddings. Notably, our proposed model exhibits a significant accuracy improvement, achieving an accuracy of 87.93%, surpassing the highest accuracy of traditional methods of 82.93% obtained with Tf-Idf and ensemble voting. Through this work, we conclude that there is a scope of improvement in disaster-related NLP work with the introduction of transformer-based architecture and Pretrained GPT which include a massive corpus of text improving the semantic coherency which in turn lead to higher evaluation metrics. This work in future can be extended to incorporate different embeddings as well as using techniques like Multi-headed attention as well as RNNs like GST, GRU, etc. © 2025 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Tweets

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SFI-Swin: symmetric face inpainting with swin transformer by distinctly learning face components distributions

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Multimedia Tools and Applications 2025年第17期84卷 17581-17595页

作者： Givkashi, MohammadHossein Naderi, MohammadReza Karimi, Nader Shirani, Shahram Samavi, Shadrokh Department of Electrical and Computer Engineering Isfahan University of Technology Isfahan84156-83111 Iran Department of Electrical and Computer Engineering McMaster University HamiltonL8S 4L8 Canada Computer Science Department Seattle University Seattle98122 United States

Image inpainting consists of filling holes or missing parts of an image. Inpainting face images with symmetric characteristics is more challenging than inpainting a natural scene. None of the powerful existing models can fill out the missing parts of an image while considering the symmetry and homogeneity of the picture. Moreover, the metrics that assess a repaired face image quality cannot measure the preservation of symmetry between the rebuilt and existing parts of a face. In this paper, we intend to solve the symmetry problem in the face inpainting task by using multiple discriminators that check each face organ’s reality separately and a transformer-based network. We also propose "symmetry concentration score" as a new metric for measuring the symmetry of a repaired face image. The quantitative and qualitative results show the superiority of our proposed method compared to some of the recently proposed algorithms in terms of the reality, symmetry, and homogeneity of the inpainted parts. The code for the proposed method is available at https://***/mohammadrezanaderi4/SFI-Swin. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Deep learning

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A Review on Smart Healthcare employing Quantum Internet of Things

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IEEE engineering Management Review 2024年 1-9页

作者： Sutradhar, Kartick Venkatesh, Ranjitha Venkatesh, Priyanka Department of Computer Science and Engineering Indian Institute of Information Technology Sri City India Department of Computer Science and Engineering GITAM School of Technology Bengaluru India Department of Computer Science and Engineering Presidency University Bengaluru India

The Quantum Internet of Things (QIoT) in the healthcare industry holds the promise of transforming patient care, diagnostics, and medical research. Quantum-enhanced sensors, communication, and computation offer unprecedented capabilities that can revolutionize how healthcare services are delivered and experienced. This paper explores the potential of QIoT in the context of smart healthcare, where interconnected quantum-enabled devices and systems create an ecosystem that enhances data security, enables real-time monitoring, and advances medical knowledge. We delve into the applications of quantum sensors in precise health monitoring, the role of quantum communication in secure telemedicine, and the computational power of quantum computing in drug discovery and personalized medicine. We discuss challenges such as technical feasibility, scalability, and regulatory considerations, along with the emerging trends and opportunities in this transformative field. By examining the intersection of quantum technologies and smart healthcare, this paper aims to shed light on the novel approaches and breakthroughs that could redefine the future of healthcare delivery and patient outcomes. IEEE

关键词： Internet of things

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Harnessing metamaterials for efficient wireless power transfer for implantable medical devices

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Bioelectronic Medicine 2024年第1期10卷 1-8页

作者： Mahmud, Sultan Nezaratizadeh, Ali Satriya, Alfredo Bayu Yoon, Yong-Kyu Ho, John S. Khalifa, Adam Department of Electrical and Computer Engineering University of Florida GainesvilleFL32611 United States Department of Electrical and Computer Engineering National University of Singapore Singapore Singapore

Wireless power transfer (WPT) within the human body can enable long-lasting medical devices but poses notable challenges, including absorption by biological tissues and weak coupling between the transmitter (Tx) and receiver (Rx). In pursuit of more robust and efficient wireless power, various innovative strategies have emerged to optimize power transfer efficiency (PTE). One such groundbreaking approach stems from the incorporation of metamaterials, which have shown the potential to enhance the capabilities of conventional WPT systems. In this review, we delve into recent studies focusing on WPT systems that leverage metamaterials to achieve increased efficiency for implantable medical devices (IMDs) in the electromagnetic paradigm. Alongside a comparative analysis, we also outline current challenges and envision potential avenues for future advancements. © The Author(s) 2024.

关键词： Metamaterials

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Hybrid Deep Learning Approach for Automating App Review Classification:Advancing Usability Metrics Classification with an Aspect-Based Sentiment Analysis Framework

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computers, Materials & Continua 2025年第1期82卷 949-976页

作者： Nahed Alsaleh Reem Alnanih Nahed Alowidi Department of Computer Science Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Computer Science College of Computer Science and EngineeringUniversity of HailHail81451Saudi Arabia

App reviews are crucial in influencing user decisions and providing essential feedback for developers to improve their *** the analysis of these reviews is vital for efficient review *** traditional machine learning(ML)models rely on basic word-based feature extraction,deep learning(DL)methods,enhanced with advanced word embeddings,have shown superior *** research introduces a novel aspectbased sentiment analysis(ABSA)framework to classify app reviews based on key non-functional requirements,focusing on usability factors:effectiveness,efficiency,and *** propose a hybrid DL model,combining BERT(Bidirectional Encoder Representations from Transformers)with BiLSTM(Bidirectional Long Short-Term Memory)and CNN(Convolutional Neural Networks)layers,to enhance classification *** analysis against state-of-the-art models demonstrates that our BERT-BiLSTM-CNN model achieves exceptional performance,with precision,recall,F1-score,and accuracy of 96%,87%,91%,and 94%,*** contributions of this work include a refined ABSA-based relabeling framework,the development of a highperformance classifier,and the comprehensive relabeling of the Instagram App Reviews *** advancements provide valuable insights for software developers to enhance usability and drive user-centric application development.

关键词： Requirements engineering(RE) app review analysis usabilitymetrics hybrid deep learning BERT-BiLSTM-CNN

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Fast Routing With Rule Caching in Lossy Mobile SDN

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Journal of Institute of Control, Robotics and Systems 2023年第11期29卷 854-858页

作者： Kim, Janghan Park, Hyung-Seok Park, Kyung-Joon Department of Electrical Engineering and Computer Science DGIST Korea Republic of

Unmanned Aerial Vehicles (UAVs) and Unmanned Ground Vehicles (UGVs) have been actively employed for tasks that are challenging for humans, such as scenarios like battlefield and disaster. Swarm unmanned systems, comprising UAVs and UGVs, experience dynamic additions or removals of unmanned aircraft. In this paper, we propose a rule caching-based recovery technique for achieving rapid forwarding rule establishment for additional UAVs deployed for recovery in wireless Software-defined Networking (SDN) based swarm unmanned systems. To validate the proposed algorithm, we conducted experiments by setting up a testbed using ODROID and a controller. Our results demonstrate that the proposed algorithm improves forwarding rule establishment performance by up to 50.3%. © ICROS 2023.

关键词： Ground vehicles

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An Encoding-Decoding Framework Based on CNN for circ RNA-RBP Binding Sites Prediction

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Chinese Journal of Electronics 2024年第1期33卷 256-263页

作者： Yajing GUO Xiujuan LEI Yi PAN School of Computer Science Shaanxi Normal University Faculty of Computer Science and Control Engineering Shenzhen Institute of Advanced TechnologyChinese Academy of Sciences Department of Computer Science Georgia State University

Predicting RNA binding protein(RBP) binding sites on circular RNAs(circ RNAs) is a fundamental step to understand their interaction mechanism. Numerous computational methods are developed to solve this problem, but they cannot fully learn the features. Therefore, we propose circ-CNNED, a convolutional neural network(CNN)-based encoding and decoding framework. We first adopt two encoding methods to obtain two original matrices. We preprocess them using CNN before fusion. To capture the feature dependencies, we utilize temporal convolutional network(TCN) and CNN to construct encoding and decoding blocks, respectively. Then we introduce global expectation pooling to learn latent information and enhance the robustness of circ-CNNED. We perform circ-CNNED across 37 datasets to evaluate its effect. The comparison and ablation experiments demonstrate that our method is superior. In addition, motif enrichment analysis on four datasets helps us to explore the reason for performance improvement of circ-CNNED.

关键词： Circular RNAs (circRNAs) RNA binding proteins Convolutional neural network Temporal convolutional network Encoder-decoder network

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