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Feature Contrast Difference and Enhanced Network for RGB-D Indoor Scene Classification in Internet of Things

IEEE Internet of Things Journal 2025年第11期12卷 17610-17621页

作者： Zhou, Wujie Jian, Bitao Liu, Yuanyuan Zhejiang University of Science and Technology School of Information and Electronic Engineering Hangzhou310023 China China University of Geosciences School of Computer Science and Technology Wuhan430074 China Nanyang Technological University College of Computing and Data Science 308232 Singapore

The era of smart connectivity spawned by the Internet of Things (IoT) has made the need to achieve environmental perception and understanding of different scenarios increasingly urgent. Among the many scenarios, indoor scene classification has attracted much attention because of its its relevance to the daily lives of people, ranging from comfort regulation in living spaces to the optimal allocation of resources in offices, and a variety of approaches for this task have emerged. However, increasing accuracy remains a crucial objective due to the complexity and disorder of indoor scenes. Therefore, we propose a feature contrast difference and enhanced network for RGB-D indoor scene classification, FCDENet. First, the RGB and Depth images express different information. Therefore, we built a feature contrast difference module for the first two low-level features to extend the receptive fields of the different features, utilizing differential contrast to complement each other. Second, the high-level feature semantic information is abstract. Therefore, we introduced information cluster blocks, which are used to aggregate feature points with similar attributes into compact clusters after being parsed by an initial frequency transform, enabling instantiated representations of the semantic information. Finally, to further enhance the integrated features, we introduced a wavelet transform block in the cross-layer decoding process. In contrast to conventional decoding methods, we employed a wavelet transform for initial denoising cross-layer features and used multiple pooling structures to supplement local information, gradually weighting to achieve higher prediction accuracy. Extensive experiments on two typical indoor datasets, NYUDv2 and SUN RGB-D, show that our results exhibit excellent performance. In addition, to better demonstrate the reliability of the method, we conducted generalizability experiments on other datasets, and the proposed method provides a robust soluti

关键词： Wavelet decomposition

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Identification and Classification of Medicinal Plants using Machine Learning Algorithms 3

Identification and Classification of Medicinal Plants using ...

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3rd International Conference on Electronics and Renewable Systems, ICEARS 2025

作者： Bhargavi, G. Jayaraman, Venkatesh Jabez, J. Deepa, R. Surendran, R. Srm Institute of Science and Technology Department of Computing Technologies Chennai India Chennai Institute of Technology Department of Computer Science and Engineering Chennai600069 India Sathyabama Institute of Science and Technology School of Computing Department of Computer Science and Engineering Chennai India Srm Institute of Science and Technology School of Computing Department of Computing Technologies Kattankulathur Tamilnadu Chennai603203 India Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798331509675

The increaing significance of plant life and botanical expertise extends beyond mere visual appreciation. With the growing interest in sustainable living and alternative remedies, there is a pressing demand for easily accessible and credible information on plant categorization, particularly focusing on medicinal plants. This innovative project synergizes machine learning capabilities, botanical proficiency, and user-friendly technology to give users the insights required to harness the benefits of medicinal plants responsibly and sustainably. The platform facilitates precise categorization and a comprehensive understanding of the therapeutic attributes of plants. © 2025 IEEE.

关键词： Medicinal chemistry

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Grover's search finds new applications in continuous optimization and spectral analysis

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science China(Physics,Mechanics & Astronomy) 2025年第6期Mechanics & Astronomy) .卷 216-217页

作者： Xiaoming Sun State Key Lab of Processors Institute of Computing Technology Chinese Academy of Sciences School of Computer Science and Technology University of Chinese Academy of Sciences

A novel quantum search algorithm tailored for continuous optimization and spectral problems was proposed recently by a research team from the University of Electronic science and Technology of China to broaden quantum computation frontiers and enrich its application *** computing has traditionally excelled at tackling discrete search challenges, but many important applications from large-scale optimization to advanced physics simulations necessitate searching through continuous domains.

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Enhancing sarcasm detection through grasshopper optimization with deep learning based sentiment analysis on social media

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International Journal of Information Technology (Singapore) 2025年第3期17卷 1785-1791页

作者： Srinu, Nidamanuri Sivaraman, K. Sriram, M. School of Computing Department of Computer Science and Engineering Bharath Institute of Higher Education and Research Chennai 600073 India

Detecting sarcasm in social media presents challenges in natural language processing (NLP) due to the informal language, contextual complexities, and nuanced expression of sentiment. Integrating sentiment analysis (SA) with sarcasm detection enhances the understanding of text meaning. Deep learning (DL), utilizing neural networks to grasp lexical and contextual features, offers a method for sarcasm detection. However, current DL-based sarcasm detection methods often overlook sentiment semantics, a crucial aspect for improving detection outcomes. Therefore, this study develops a new sarcasm detection using grasshopper optimization algorithm with DL (SD-GOADL) technique. The SD-GOADL technique aims to explore the patterns that exist in social media data and detect sarcasm. To obtain this, the SD-GOADL technique undergoes data pre-processing and Glove based word embedding technique. Next, the classification of sarcasm takes place using deep belief network (DBN) system. For enhancing the detection results of the DBN approach, the SD-GOADL technique uses GOA for hyperparameter selection process. The stimulation outcome of the SD-GOADL technique is tested on a sarcasm dataset and the results highlight the significant performance of the SD-GOADL technique compared to recent models. © Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： Deep learning Grasshopper optimization algorithm Natural language processing Sarcasm detection Sentiment analysis

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Integrating Edge Intelligence with Blockchain-Driven Secured IoT Healthcare Optimization Model

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computers, Materials & Continua 2025年第5期83卷 1973-1986页

作者： Khulud Salem Alshudukhi Mamoona Humayun Ghadah Naif Alwakid Department of Computer Science College of Computer and Information SciencesJouf UniversitySakaka72388Saudi Arabia Department of Computing School of Arts Humanities and Social SciencesUniversity of RoehamptonLondonSW155PHUK

The Internet ofThings(IoT)and edge computing have substantially contributed to the development and growth of smart *** handled time-constrained services and mobile devices to capture the observing environment for surveillance *** systems are composed of wireless cameras,digital devices,and tiny sensors to facilitate the operations of crucial healthcare ***,many interactive applications have been proposed,including integrating intelligent systems to handle data processing and enable dynamic communication functionalities for crucial IoT ***,most solutions lack optimizing relayingmethods and impose excessive overheads for maintaining devices’***,data integrity and trust are another vital consideration for nextgeneration *** research proposed a load-balanced trusted surveillance routing model with collaborative decisions at network edges to enhance energymanagement and resource *** leverages graph-based optimization to enable reliable analysis of decision-making ***,mobile devices integratewith the proposed model to sustain trusted routes with lightweight privacy-preserving and *** proposed model analyzed its performance results in a simulation-based environment and illustrated an exceptional improvement in packet loss ratio,energy consumption,detection anomaly,and blockchain overhead than related solutions.

关键词： Smart cities load balancing blockchain health systems edge computing

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Online Queue-Aware Service Migration and Resource Allocation in Mobile Edge computing

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IEEE Transactions on Vehicular Technology 2025年第5期74卷 8063-8078页

作者： Du, An Jia, Jie Chen, Jian Wang, Xingwei Huang, Ming Northeastern University School of Computer Science and Engineering Engineering Research Center of Security Technology of Complex Network System Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Shenyang110819 China Northeastern University School of Computer Science and Engineering Shenyang110819 China

Mobile edge computing (MEC) integrated with Network Functions Virtualization (NFV) helps run a wide range of services implemented by Virtual Network Functions (VNFs) deployed at MEC networks. This emerging paradigm offers flexible edge resource management for Internet Service Providers (ISPs) and improves service satisfaction of diverse applications. However, a critical challenge lies in processing extremely unpredictable and bursty traffic flow, especially in event-trigger sensing tasks. In this paper, we study queue-aware service migration and resource allocation in an MEC network, where different devices with different arrival patterns request different network services. To this end, we first formulate a novel long-term operation cost minimization problem subject to the stability of multiple queues by adopting a simultaneous migration scheme and determining resource allocation dynamically. To address the problem efficiently, we propose an online control framework to make decisions without any prior system information. We first adopt the Lyapunov optimization technique to decompose the long-term optimization problem into multi-per-slot subproblems. Then, we adopt a decoupled optimization algorithm based on the coordinate descent method and augmented Lagrangian method to solve the offline mixed integer non-linear programming (MINLP) subproblems. Extensive results reveal that the proposed online optimization framework can efficiently balance queue stability and cost consumption. © 1967-2012 IEEE.

关键词： Mobile edge computing

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NL2Code: A Hybrid NLP and Model-Driven Framework for Automated Code Generation from Natural Language and UML

NL2Code: A Hybrid NLP and Model-Driven Framework for Automat...

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2025 IEEE International Students' Conference on Electrical, Electronics and computer science, SCEECS 2025

作者： Nair, Reshma P Thushara, M.G. Amrita School of Computing Amrita Department of Computer Science & Engineering Kerala Vishwa Vidyapeetham Amritapuri Kollam India Amrita School of Computing Amrita Department of Computer Science & Applications Kerala Vishwa Vidyapeetham Amritapuri Kollam India

ISBN: (纸本)9798331529833

The process of converting natural language requirements and visual models into executable software code remains an ongoing challenge in software engineering. We developed an intelligent system that adopts Natural Language Processing (NLP) and Model-Driven Engineering (MDE) to generate code from natural language requirements and UML diagrams. It then applies advanced NLP techniques for natural language specification, such as tokenization, POS tagging, semantic analysis, and information extraction. This information can be transformed into computer language construct and design patterns with the help of MDE concepts. It generates code templates, skeleton code, and real code and incorporates key principles of software engineering and technological specializations. Case studies explain how the system can improve the software development lifecycle for a development company by automatically translating the natural language and the visual models into code. The system synthesized code with 92-95 % accuracy and 92-93 % completeness in three particular cases of use, proving the efficiency of the translation process of natural language requirements and UML diagrams into executable code. The present work enriches software engineering, NLP, as well as the integration of MDE, and code generation, reducing time consumption, manual efforts, and fast communication in the Software Development Process. © 2025 IEEE.

关键词： Semantics

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Lower Bounds for Approximate (& Exact) k-Disjoint-Shortest-Paths 22nd

Lower Bounds for Approximate (& Exact) k-Disjoint-Shortest-...

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22nd International Workshop on Approximation and Online Algorithms, WAOA 2024

作者： Chitnis, Rajesh Thomas, Samuel Wirth, Anthony School of Computer Science The University of Birmingham Birmingham United Kingdom School of Computing and Information Systems The University of Melbourne Melbourne Australia School of Computer Science The University of Sydney Sydney Australia

ISBN: (纸本)9783031813955

Given a graph G=(V,E) and a set T={(si,ti):1≤i≤k}⊆V×V of k pairs, the k-Vertex-Disjoint-Paths (resp. k-Edge-Disjoint-Paths) problem asks to determine whether there exist k pairwise vertex-disjoint (resp. edge-disjoint) paths P1,P2,…,Pk in G such that, for each 1≤i≤k, Pi connects si to ti. Both the edge-disjoint and vertex-disjoint versions in undirected graphs are famously known to be FPT (parameterized by k) due to the Graph Minor Theory of Robertson and Seymour. Eilam-Tzoreff [DAM ‘98] introduced a variant, known as the k-Disjoint-Shortest-Paths problem, where each path is further required to be a shortest path connecting its pair. They showed that the k-Disjoint-Shortest-Paths problem is NP-complete on both directed and undirected graphs;this holds even if the graphs are planar and have unit edge lengths. We focus on four versions of the problem, corresponding to considering edge/vertex disjointness, and to considering directed/undirected graphs. Building on the reduction of Chitnis [SIDMA ’23] for k-Edge-Disjoint-Paths on planar DAGs, we obtain the following inapproximability lower bound for each of the four versions of k-Disjoint-Shortest-Paths on n-vertex graphs:Under the gap version of the Exponential Time Hypothesis (Gap-ETH), there exists a constant δ>0 such that for any constant 0δ·k time. Under the gap version of the Exponential Time Hypothesis (Gap-ETH), there exists a constant δ>0 such that for any constant 0δ·k time. We provide a single, unified framework to obtain lower bounds for each of the four versions of k-Disjoint-Shortest-Paths. We are able to further strengthen our results by restricting the structure of the input graphs in the lower bound constructions as follows:Directed: The inapproximability lower bound for edge-disjoint (resp. vertex-disjoint) paths holds even if the input graph is a planar (resp. 1-planar) DAG with max in-degree and max out-degree at most ***: The inapproximability lower bound for edge-disjoint (resp. vertex-dis

关键词： Undirected graphs

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Model Recovery in Federated Unlearning With Restricted Server Data Resources

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IEEE Internet of Things Journal 2025年第11期12卷 17920-17935页

作者： Zhang, Jianxin Zhao, Mengda Wang, Zhenwei Su, Weijian Wang, Pengfei Dalian Minzu University School of Computer Science and Engineering Dalian116600 China Dalian University of Technology School of Computer Science and Technology Dalian116024 China Ministry of Education Key Laboratory of Social Computing and Cognitive Intelligence Dalian University of Technology Dalian116024 China

Recent model recovery methods in federated unlearning (FUL) either rely on additional communication with the remaining clients or require large amounts of high-quality data from the server for training, overlooking scenarios with limited data resources. Currently, Contrastive Language-Image Pretraining (CLIP) has demonstrated remarkable performance across a wide range of tasks, particularly excelling in few-shot learning scenarios. In this paper, inspired by CLIP, we explore the scenario of few-shot knowledge distillation and propose CLIP-Guided Few-shot Knowledge Distillation (CGKD) for Model Recovery in FUL. CGKD mainly consists of three components: 1) The unlearning module constructs the unlearning model by erasing all historical contributions of the target client, and this model is treated as the student model;2) Fine-tuning the pre-trained CLIP model using few-shot data from the server side to obtain a more robust teacher model (CLIP*);and 3) Model recovery is achieved through knowledge distillation, leveraging the rich visual and semantic knowledge of CLIP∗ to enhance the student model's understanding of image semantic context, thereby improving the performance of the unlearning model. Extensive experimental results demonstrate that CGKD outperforms the compared FUL method in recovery performance across four standard datasets, validating the effectiveness of our approach. © 2014 IEEE.

关键词： Federated learning

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Comprehensive Web Analysis of Automobile Websites: Performance, SEO, and Accessibility Insights

Comprehensive Web Analysis of Automobile Websites: Performan...

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2025 IEEE International Conference on Computational, Communication and Information Technology, ICCCIT 2025

作者： Busetty, Abhishek Balam, Ruchith Chillakuru, Hari Radha, R. Saranya Devi, B. Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798331512965

The current research work proposes a difficultydriven comparison of three most visited automobile websites namely the Automobile Site1, Automobile Site2, Automobile Site3 on parameters including performance, Search engine optimization (SEO), accessibility and traffic. The research involves tool such as Yellow Labs, Google Page Speed Insight and Semrush with regard to the website's weight, loading time, user's interactions and search engine optimization. The findings reveal distinct strengths across the sites: From the three Automobile Site1 is the most optimized and accessibly friendly site, the factors of search engine friendliness is as follows. Looking at the traffic and users of Automobile Site2, it comfortably outclasses the other sites in terms of page views as well as the audience loyalty. At the same time, Automobile Site3 has relatively low first load time which indicates fast website browsing while it is still relatively weak in terms of its SEO and the usability of the site has to be improved in terms of theoretical potential. In conclusion, the realized comparison exposes the strengths and weaknesses of each site and is informative to future developments of the sites with an aim at better user interaction levels and competitive advantage. © 2025 IEEE.

关键词： Automobiles

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