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Two-Tier Clustering with Routing Protocol for IoT Assisted WSN

computers, Materials & Continua 2022年第5期71卷 3375-3392页

作者： AArokiaraj Jovith Mahantesh Mathapati M.Sundarrajan N.Gnanasankaran Seifedine Kadry Maytham N.Meqdad Shabnam Mohamed Aslam Department of Networking and Communications College of Engineering and Technology SRM Institute of Science and TechnologyKattankulathur603203India Department of Computer Science and Engineering Rajarajeswari College of EngineeringBengaluru560074India Department of Computer Science and Engineering K.Ramakrishnan College of EngineeringTiruchirappalli621112India Department of Computer Science Thiagarajar CollegeMadurai625019India Department of Applied Data Science Noroff University CollegeKristiansandNorway Al-Mustaqbal University College HillahIraq Department of Information Technology College of Computing and Information SciencesMajmaah UniversityAl Majmaah11952Saudi Arabia

In recent times,Internet of Things(IoT)has become a hot research topic and it aims at interlinking several sensor-enabled devices mainly for data gathering and tracking *** Sensor Network(WSN)is an important component in IoT paradigm since its inception and has become the most preferred platform to deploy several smart city application areas like home automation,smart buildings,intelligent transportation,disaster management,and other such IoT-based *** methods are widely-employed energy efficient techniques with a primary purpose i.e.,to balance the energy among sensor *** and routing processes are considered as Non-Polynomial(NP)hard problems whereas bio-inspired techniques have been employed for a known time to resolve such *** current research paper designs an Energy Efficient Two-Tier Clustering with Multi-hop Routing Protocol(EETTC-MRP)for IoT *** presented EETTC-MRP technique operates on different stages namely,tentative Cluster Head(CH)selection,final CH selection,and *** first stage of the proposed EETTC-MRP technique,a type II fuzzy logic-based tentative CH(T2FL-TCH)selection is ***,Quantum Group Teaching Optimization Algorithm-based Final CH selection(QGTOA-FCH)technique is deployed to derive an optimum group of CHs in the ***,Political Optimizer based Multihop Routing(PO-MHR)technique is also employed to derive an optimal selection of routes between CHs in the *** order to validate the efficacy of EETTC-MRP method,a series of experiments was conducted and the outcomes were examined under distinct *** experimental analysis infers that the proposed EETTC-MRP technique is superior to other methods under different measures.

关键词： Wireless networks internet of things energy efficiency clustering multi-hop routing metaheuristics

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Recent progress in hydrogen:From solar to solar cell

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Journal of Materials science & technology 2024年第9期176卷 236-257页

作者： Yanlin Li Zhu Ma Shanyue Hou Qianyu Liu Guangyuan Yan Xiaoshan Li Tangjie Yu Zhuowei Du Junbo Yang Yi Chen Wei You Qiang Yang Yan Xiang Shufang Tang Xuelin Yue Meng Zhang Wenfeng Zhang Jian Yu Yuelong Huang Jiale Xie Chun Tang Yaohua Mai Kuan Sun School of New Energy and Materials Southwest Petroleum University(SWPU)Chengdu 610500China Institute of New Energy Technology College of Information Science and TechnologyJinan UniversityGuangzhou 510632China Key Laboratory of Low-grade Energy Utilization Technologies and Systems(MoE) School of Energy and Power EngineeringChongqing UniversityChongqing 400044China

Hydrogen,meeting the requirements of sustainable development,is regarded as the ultimate energy in the 21st *** to the inexhaustible and feasible of solar energy,solar water splitting is an immensely promising strategy for environmental-friendly hydrogen production,which not only overcomes the fluctuation and intermittency but also contributes to achieving the mission of global“Carbon Neutrality and Carbon Peaking”.However,there is still a lack of a comprehensive overview focusing on hydrogen progress with a discussion of development from solar energy to solar ***,we emphasize several solar-to-hydrogen pathways from the basic concepts and principles and focus on photovoltaic-electrolysis and photoelectrochemical/photovoltaic systems,which have achieved solar-to-hydrogen(STH)efficiency of over 10%and have extremely promising for large-scale *** addition,we summarize the challenges and opportunities faced in this field including configuration design,electrode materials,and performance ***,perspectives on the potential commercial application and scientific research for the further development of solar-to-hydrogen are analyzed and presented.

关键词： Hydrogen Photovoltaics Catalysis STH efficiency Integrated module

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Intelligent Decision-making Algorithm for IoT Enabled Smart shopping

Intelligent Decision-making Algorithm for IoT Enabled Smart ...

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2024 International Conference on Communication, Computing and Internet of Things, IC3IoT 2024

作者： Mythili, J. Menmoli, S.T. Dhanusuya, S. Sethuram, M. K. S. Rangasamy College Of Technology Department Of Computer Science And Engineering Tiruchengode637 215 India

ISBN: (纸本)9798350352689

In today's fast-changing retail industry, IoT and data-driven analytics have changed consumer buying and business operations. In IoT-enabled smart shopping environments, intelligent decision-making algorithms are crucial. This paper presents an intelligent decision-making system for IoT-enabled smart purchasing. The program improves various shopping aspects using real-time data from IoT sensors, RFID tags, and cameras. The algorithm includes data integration, advanced analytics, decision optimization, real-time adaptation, and customization. The programme provides actionable information from customer behaviour, product inventories, and environmental elements to aid decision-making. These choices include inventory management, pricing, product recommendations, and targeted promotions to enhance the shopping experience. The real-time algorithm adapts to demand, inventory, and client preferences. Personalized recommendations and promotions boost retailer revenue and consumer engagement. The algorithm streamlines inventory management, improving efficiency, cost, and retail competitiveness. © 2024 IEEE.

关键词： Decision making

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Proposed Biometric Security System Based on Deep Learning and Chaos Algorithms

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computers, Materials & Continua 2023年第2期74卷 3515-3537页

作者： Iman Almomani Walid El-Shafai Aala AlKhayer Albandari Alsumayt Sumayh S.Aljameel Khalid Alissa Security Engineering Lab Computer Science DepartmentPrince Sultan UniversityRiyadh11586Saudi Arabia Computer Science Department King Abdullah II School of Information TechnologyThe University of Jordan11942Jordan Department of Electronics and Electrical Communications Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Computer Science Department Applied CollegeImam Abdulrahman Bin Faisal UniversityP.O.Box 1982Dammam31441Saudi Arabia Computer Science Department College of Computer Science and Information TechnologyImam Abdulrahman Bin Faisal UniversityP.O.Box 1982Dammam31441Saudi Arabia SAUDI ARAMCO Cybersecurity Chair Networks and Communications DepartmentCollege of Computer Science and Information TechnologyImam Abdulrahman Bin Faisal UniversityP.O.Box 1982Dammam31441Saudi Arabia

Nowadays,there is tremendous growth in biometric authentication and cybersecurity ***,the efficient way of storing and securing personal biometric patterns is mandatory in most governmental and private ***,designing and implementing robust security algorithms for users’biometrics is still a hot research area to be *** work presents a powerful biometric security system(BSS)to protect different biometric modalities such as faces,iris,and *** proposed BSSmodel is based on hybridizing auto-encoder(AE)network and a chaos-based ciphering algorithm to cipher the details of the stored biometric patterns and ensures their *** employed AE network is unsupervised deep learning(DL)structure used in the proposed BSS model to extract main biometric *** obtained features are utilized to generate two random chaos *** first random chaos matrix is used to permute the pixels of biometric *** contrast,the second random matrix is used to further cipher and confuse the resulting permuted biometric pixels using a two-dimensional(2D)chaotic logisticmap(CLM)*** assess the efficiency of the proposed BSS,(1)different standardized color and grayscale images of the examined fingerprint,faces,and iris biometrics were used(2)comprehensive security and recognition evaluation metrics were *** assessment results have proven the authentication and robustness superiority of the proposed BSSmodel compared to other existing *** example,the proposed BSS succeeds in getting a high area under the receiver operating characteristic(AROC)value that reached 99.97%and low rates of 0.00137,0.00148,and 3516 CMC,2023,vol.74,no.20.00157 for equal error rate(EER),false reject rate(FRR),and a false accept rate(FAR),respectively.

关键词： Biometric security deep learning AE network 2D CLM cybersecurity and authentication applications feature extraction unsupervised learning

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Learning Well-Separated and Representative Prototypes for Few-Shot Event Detection 12th

Learning Well-Separated and Representative Prototypes for ...

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12th National CCF Conference on Natural Language Processing and Chinese Computing, NLPCC 2023

作者： Zhang, Xintong Li, Shasha Ji, Bin Wang, Ting College of Computer Science and Technology National University of Defense Technology Changsha410073 China National University of Singapore Singapore Singapore

ISBN: (纸本)9783031446955

Event detection can be solved with two subtasks: identification and classification of trigger words. Depending on whether these two subtasks are handled simultaneously, event detection models are divided into the pipeline-based paradigm and the joint-based paradigm. For both paradigms, prototypical networks are an extremely important method to solve a few-shot event detection task. Prototypical networks classify through the distances between instances and prototypes. Therefore, both the distance between prototypes and prototype representations are the key to prototypical networks. Compared with the pipeline-based paradigm, the joint-based paradigm handles two subtasks at the same time to effectively avoid the error cascade. To make better use of prototypical networks in the joint-based paradigm, we propose PN-SR, a Prototypical Network with well-Separated and Representative prototypes. Specifically, we use representation learning methods to generate prototypes. At the same time, we add a loss factor to further enlarge the distances between prototypes of different classes and reduce semantic representation distances of instances with the same class. Experiments show our method outperforms many baselines by at least 26.15% F1 points under the same few-shot settings and achieves SOTA. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Pipelines

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Long Short-Term Memory-Based Next Keyword Prediction 6

Long Short-Term Memory-Based Next Keyword Prediction

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6th IEEE International Conference on Emerging Smart Computing and Informatics, ESCI 2024

作者： Rajkumar, K. Karthikeyan, S. Hariharan, U. College of Engineering and Technology SRM Institute of Science and Technology Dept. of Data Science and Business Systems Tamilnadu Kattankulathur India Department of CSE Bangalore562112 India Apex Institute of Technology Chandigarh University Department of CSE Punjab India

ISBN: (纸本)9798350306613

This paper focuses on a method that uses an LSTM model to predict the next word of a sentence. Based on Katz's Backoff model, it has been designed to improve and further succeed in our previous work. The main intention here is to show how effective the LSTM model is when compared to Katz's backoff model in terms of the number of n-grams required to predict the next word, and it also helps you predict more accurately. Here LSTM is used for processing, prediction and classification. Since the LSTM model is efficient and has been modified to the task at hand, the model offers very highly accurate predictions even though have used only a tiny portion of the entire corpus. When comparing the existing model, the proposed model predicts the word with accuracy of 90%. © 2024 IEEE.

关键词： Katz's backoff Model LSTM Prediction

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Predictive Modeling for Early Cancer Detection: A Machine Learning Approach to Prostate, Lung, and Breast Cancer 2

Predictive Modeling for Early Cancer Detection: A Machine Le...

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2nd DMIHER International Conference on Artificial Intelligence in Healthcare, Education and Industry, IDICAIEI 2024

作者： Rokde, Chandrayani Kharwandikar, Prajakta Kungwani, Amrita Dudhe, Anshu R. Yeshwantrao Chavhan College of Engineering Department of Information Technology Maharashtra Nagpur440001 India Indian Institute of Information Technology Department of Computer Science & Engineering Maharashtra Nagpur440001 India Jhulelal Institute of Technology Department of Computer Science & Technology Maharashtra Nagpur440001 India Nc Pharmacy Wanadongri Maharashtra Nagpur440001 India

ISBN: (纸本)9798331528713

Improving patient outcomes and lowering death rates from cancer need early identification. In this study, we use machine learning algorithms to predict three forms of cancer: prostate, lung, and breast. Lung cancer continues to be the primary cause of cancer-related mortality, while breast cancer affects women significantly worldwide. Prostate cancer is prevalent among men. By harnessing the power of machine learning techniques, our goal is to develop predictive models that can identify these cancers at an early stage, facilitating prompt intervention and treatment. Cancer research is instrumental in supporting medical treatments for patients, and machine learning is increasingly playing a vital role in cancer detection and diagnosis. As technology advances, the future holds the promise of easier cancer prediction without the need for hospital visits. Our project aims to determine the most effective algorithm among SVM and Random Forest for predicting cancer. We consider distinct attributes tailored to each cancer type, such as clump thickness for breast cancer, lifestyle factors for lung cancer, and physical attributes for prostate cancer. Users input relevant data, and our models provide predictions, such as determining benign and estimating the likelihood of being affected by lung or prostate cancer. © 2024 IEEE.

关键词： Lung cancer

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Fast MRI Reconstruction via Boosting Filter Diversity of Deep Cascading Networks

Fast MRI Reconstruction via Boosting Filter Diversity of Dee...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Zheng, Wen-Hao Li, Xiao-Xin Hu, Haigen Zhou, Qianwei Wang, Qihui Zhejiang University of Technology College of Computer Science and Technology Hangzhou China Hangzhou Normal University School of Engineering Hangzhou China

ISBN: (纸本)9798350337488

Deep Cascading Networks (DCNs) are very popular for fast MRI reconstruction. However, DCNs still have limited generalization ability on highly undersampled MRI data. One main reason is that the training data is not well used. A promising solution is to boost the filter diversity of DCNs to well fit the rich features in the training data. This can be achieved by reducing the repetition level of the undersampled input images via using the pixel unshuffle (PU) operator. As different input images and different subnets of DCNs require different PU-scales, we propose a novel PU-Scale Estimation (PUSE) method to automatically infer optimal PU-scales. By incorporating PUSE into DCNs, we construct a new Multi-PU-Scale Diversity based (MSDiv+) architecture for DCNs. To boost training convergence, we further propose to generate mini-batches by mixing data samples with different optimal PU-scales. Experiments on the fastMRI dataset demonstrate the effectiveness of our method. © 2023 IEEE.

关键词： deep cascading networks filter diversity MRI Reconstruction pixel unshuffle

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Fundus Image Registration Based on Improved Feature Description 2nd

Fundus Image Registration Based on Improved Feature Descript...

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2nd International Conference on Image, Vision and Intelligent systems, ICIVIS 2022

作者： Hang, Yiliu Zhang, Qiong Qiu, Jianlin College of Computer and Information Engineering Nantong Institute of Technology Nantong226000 China College of Information Science and Technology Nantong University Nantong226000 China

ISBN: (纸本)9789819909223

Aiming at low-contrast fundus images directly captured by fundus instrument, a fundus image registration method based on improved feature description was proposed to improve the accuracy and efficiency of registration. First, pixel-level feature points were detected, and the sub-pixel position was calculated by Zernike Matrix. Then, an improved multi-disk mask feature description technique based on the FREAK algorithm is introduced to describe the feature points. Finally, the Euclidean distance combined with random sampling consistency algorithm is used to purify the matching points and calculate the transformation matrix to carry out the final image registration. The experimental results show that the proposed registration method can be used in fundus screening image database, especially in low-contrast fundus images. Compared with SIFT (Scale-invariant Feature Transform), SURF (Speeded-Up Robust Features) and FREAK (Fast Retina Keypoint) algorithms, the new method has more advantages in registration effect, efficiency and speed. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Image registration

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OneActor: Consistent Subject Generation via Cluster-Conditioned Guidance 38

OneActor: Consistent Subject Generation via Cluster-Conditio...

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38th Conference on Neural Information Processing systems, NeurIPS 2024

作者： Wang, Jiahao Yan, Caixia Lin, Haonan Zhang, Weizhan Wang, Mengmeng Gong, Tieliang Dai, Guang Sun, Hao School of Computer Science and Technology MOEKLINNS Xi'an Jiaotong University China State Key Laboratory of Communication Content Cognition China College of Computer Science and Technology Zhejiang University of Technology China SGIT AI Lab State Grid Corporation of China Switzerland China Telecom Artificial Intelligence Technology Co.Ltd. China

Text-to-image diffusion models benefit artists with high-quality image generation. Yet their stochastic nature hinders artists from creating consistent images of the same subject. Existing methods try to tackle this challenge and generate consistent content in various ways. However, they either depend on external restricted data or require expensive tuning of the diffusion model. For this issue, we propose a novel one-shot tuning paradigm, termed OneActor. It efficiently performs consistent subject generation solely driven by prompts via a learned semantic guidance to bypass the laborious backbone tuning. We lead the way to formalize the objective of consistent subject generation from a clustering perspective, and thus design a cluster-conditioned model. To mitigate the overfitting challenge shared by one-shot tuning pipelines, we augment the tuning with auxiliary samples and devise two inference strategies: semantic interpolation and cluster guidance. These techniques are later verified to significantly improve the generation quality. Comprehensive experiments show that our method outperforms a variety of baselines with satisfactory subject consistency, superior prompt conformity as well as high image quality. Our method is capable of multi-subject generation and compatible with popular diffusion extensions. Besides, we achieve a 4× faster tuning speed than tuning-based baselines and, if desired, avoid increasing the inference time. Furthermore, our method can be naturally utilized to pre-train a consistent subject generation network from scratch, which will implement this research task into more practical applications. Project page: https://***/OneActor-webpage/. © 2024 Neural information processing systems foundation. All rights reserved.

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