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Wind Driven Optimization-Based Medical Image Encryption for Blockchain-Enabled Internet of Things Environment

computers, Materials & Continua 2022年第11期73卷 3219-3233页

作者： C.S.S.Anupama Raed Alsini N.Supriya E.Laxmi Lydia Seifedine Kadry Sang-Soo Yeo Yongsung Kim Department of Electronics&Instrumentation Engineering V.R.Siddhartha Engineering CollegeVijayawada520007India Department of Information Systems Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddahSaudi Arabia Department of Computer Science and Engineering Malla Reddy Engineering College(A)Hyderabad500100India Department of Computer Science and Engineering Vignan’s Institute of Information TechnologyVisakhapatnam530049India Department of Applied Data Science Noroff University College4612KristiansandNorway Department of Computer Engineering Mokwon UniversityDaejeon35349Korea Department of Technology Education Chungnam National University 99Daehak-roYuseong-guDaejeon34134Korea

Internet of Things(IoT)and blockchain receive significant interest owing to their applicability in different application areas such as healthcare,finance,transportation,*** image security and privacy become a critical part of the healthcare sector where digital images and related patient details are communicated over the public *** paper presents a new wind driven optimization algorithm based medical image encryption(WDOA-MIE)technique for blockchain enabled IoT *** WDOA-MIE model involves three major processes namely data collection,image encryption,optimal key generation,and data ***,the medical images were captured from the patient using IoT ***,the captured images are encrypted using signcryption *** addition,for improving the performance of the signcryption technique,the optimal key generation procedure was applied by WDOA *** goal of the WDOA-MIE algorithm is to derive a fitness function dependent upon peak signal to noise ratio(PSNR).Upon successful encryption of images,the IoT devices transmit to the closest server for storing it in the blockchain *** performance of the presented method was analyzed utilizing the benchmark medical image *** security and the performance analysis determine that the presented technique offers better security with maximum PSNR of 60.7036 dB.

关键词： Internet of things image security medical images encryption optimal key generation blockchain

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The Smart Coverage Path Planner for Autonomous Drones Using TSP and Tree Selection 1

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9th International Conference on Mining Intelligence and Knowledge Exploration, MIKE 2023

作者： Sundarrajan, M. Jothi, Akshya Prabakar, D. Kadry, Seifedine SRM Institute of Science and Technology Chennai India Department of Applied Data Science Noroff University College Kristiansand Norway Ajman University Ajman346 United Arab Emirates Department of Electrical and Computer Engineering Lebanese American University Byblos Lebanon

ISBN: (数字)9783031440847

ISBN: (纸本)9783031440830

The problem of efficiently covering an area with a limited number of sensors or cameras is an important research topic in various fields such as surveillance, monitoring, and inspection. This problem can be posed as a Traveling Salesman Problem (TSP), where the optimal path needs to be found to visit all the coverage points and return to the starting point. However, the computational complexity of solving TSP increases exponentially with the number of coverage points, making it impractical for large-scale applications. This paper proposes a novel approach to solving the coverage problem as a TSP by incorporating tree selection algorithms to improve efficiency and accuracy. The proposed approach represents the study area as a graph, with each node representing a coverage point and the edges representing the distance between them. To reduce computational complexity, we use various tree selection algorithms to select the best possible starting point for the TSP algorithm. To evaluate the effectiveness of the proposed approach, we conduct simulations using different scenarios with varying numbers of coverage points. The simulation results demonstrate that the proposed approach significantly improves the quality of coverage while reducing computational complexity compared to traditional TSP algorithms. Moreover, the proposed approach can be applied in a wide range of real-world applications, such as surveillance, monitoring, or inspection, where efficient and accurate coverage of an area is essential. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Drones

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An Automated Deep Learning Based Muscular Dystrophy Detection and Classification Model

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computers, Materials & Continua 2022年第4期71卷 305-320页

作者： T.Gopalakrishnan Periakaruppan Sudhakaran K.C.Ramya K.Sathesh Kumar Fahd N.Al-Wesabi Manal Abdullah Alohali Anwer Mustafa Hilal School of Computer Science and Engineering Vellore Institute of TechnologyVellore632014India Department of Computer Science and Engineering SRM TRP Engineering CollegeTiruchirappalli621105India Department of Electrical and Electronics Engineering Sri Krishna College of Engineering and TechnologyCoimbatore641008India School of Computing Kalasalingam Academy of Research and EducationKrishnankoil626128India Department of Computer Science King Khalid UniversityMuhayel AseerKSA Faculty of Computer and IT Sana’a UniversityYemen Department of Information Systems College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversitySaudi Arabia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlkharjSaudi Arabia

Muscular Dystrophy (MD) is a group of inherited muscular diseases that are commonly diagnosed with the help of techniques such asmuscle biopsy, clinical presentation, and Muscle Magnetic Resonance Imaging(MRI). Among these techniques, Muscle MRI recommends the diagnosis ofmuscular dystrophy through identification of the patterns that exist in musclefatty replacement. But the patterns overlap among various diseases whereasthere is a lack of knowledge prevalent with regards to disease-specific ***, artificial intelligence techniques can be used in the diagnosis ofmuscular dystrophies, which enables us to analyze, learn, and predict forthe future. In this scenario, the current research article presents an automated muscular dystrophy detection and classification model using SynergicDeep Learning (SDL) method with extreme Gradient Boosting (XGBoost),called SDL-XGBoost. SDL-XGBoost model has been proposed to act as anautomated deep learning (DL) model that examines the muscle MRI dataand diagnose muscular dystrophies. SDL-XGBoost model employs Kapur’sentropy based Region of Interest (RoI) for detection purposes. Besides, SDLbased feature extraction process is applied to derive a useful set of featurevectors. Finally, XGBoost model is employed as a classification approach todetermine proper class labels for muscle MRI data. The researcher conductedextensive set of simulations to showcase the superior performance of SDLXGBoost model. The obtained experimental values highlighted the supremacyof SDL-XGBoost model over other methods in terms of high accuracy being96.18% and 94.25% classification performance upon DMD and BMD respectively. Therefore, SDL-XGBoost model can help physicians in the diagnosis of muscular dystrophies by identifying the patterns of muscle fatty replacementin muscle MRI.

关键词： Muscle magnetic resonance imaging XGBoost synergic deep learning roI detection kapur’s entropy muscular dystrophies

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Cloud-Based Foot Pressure Analysis for Diabetic Care Using ANN 5

Cloud-Based Foot Pressure Analysis for Diabetic Care Using A...

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5th International Conference on data Intelligence and Cognitive Informatics, ICDICI 2024

作者： Pradeepa, H. Amarnath, Raveendra N. Sivakumar, V.G. Thamizhamuthu, R. Manikandan, G. Bhuvaneswari, G. Thiagarajar College of Engineering Department of Electronics and Communication Engineering Tamil Nadu Madurai India Mphasis Campbell Dr MelissaTX United States Vidya Jyothi Institute of Technology Department of Electronics and Communication Engineering Telangana Hyderabad India Srm Institute of Science and Technology Department of Computing Technologies Kattankulathur Campus-Chengalpattu Tamil Nadu Chennai India Rmk Engineering College Department of Artificial Intelligence and Data Science RSM Nagar Tamil Nadu Chennai India Saveetha Engineering College Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350389609

It introduces a new method for caring for diabetic feet combines smart pressure monitoring for the feet with wearable gear that is cloud-based and uses artificial neural networks (ANNs). It is essential to recognize diabetic foot ulcers early and monitor them continuously to avoid complications. Wearable pressure sensors record data on the user's foot pressure in real time make up the proposed system. ANNs trained to detect patterns recommend a possible risk of ulceration by analyzing this data once it is transferred to a cloud-based platform. By using cloud-based architecture, healthcare practitioners can remotely monitor patients and intervene as necessary. Clinical trials show the technology improves diabetic foot care by detecting irregularities in pressure distribution and lowering the risk of ulcers and their sequel. Improved patient outcomes and decreased healthcare expenses are achieved via the preventive treatment of diabetic feet made possible by this system, which makes use of breakthroughs in wearable technology and artificial intelligence (AI). © 2024 IEEE.

关键词： Remote patient monitoring

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Machine Learning for Predicting Energy Consumption in Smart Buildings Using Artificial Adaptive Systems

Machine Learning for Predicting Energy Consumption in Smart ...

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2025 International Conference on Multi-Agent Systems for Collaborative Intelligence, ICMSCI 2025

作者： Kocherla, Raviteja Jency, C. Mohan, T. Mani Mallala, Balasubbareddy Manikandaprabu, P. Kanth, Tatiraju V. Rajani Mallareddy University Department of Computer Science & Engineering Telangana Hyderabad500043 India Dr. N.G.P. Institute of Technology Department of Artificial Intelligence and Data Science Coimbatore641048 India Annamacharya University Department of Mechanical Engineering Rajampet516126 India ChaitanyaBharathi Institute of Technology Department of Electrical and Electronics Engineering Telangana Hyderabad500075 India Sona College of Technology Department of Mechanical Engineering Salem636005 India Tvr Consulting Services Private Limited Telangana Hyderabad500040 India

ISBN: (纸本)9798331509828

This study investigates the use of Artificial Adaptive (AA) systems for predicting energy consumption in Smart Buildings, focusing on optimizing energy usage with sensors, actuators, and intelligent systems. Using the CRISP-DM methodology, the research incorporated statistical techniques like PCA and ARIMA models to extract relevant features and identify temporal relationships. Long Short-Term Memory (LSTM) networks were employed to refine prediction models, balancing neurons and training epochs to prevent overfitting or underfitting. The best model, utilizing the first Principal Component, achieved 74% precision. The findings highlight AA systems' potential in energy management, emphasizing the challenges of model precision and resource availability, while offering a foundation for future improvements in predictive modeling. © 2025 IEEE.

关键词： Smart homes

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Carrying Out CNN Channel Pruning in a White Box

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IEEE Transactions on Neural Networks and Learning Systems 2023年第10期34卷 7946-7955页

作者： Zhang, Yuxin Lin, Mingbao Lin, Chia-Wen Chen, Jie Wu, Yongjian Tian, Yonghong Ji, Rongrong Xiamen University Media Analytics and Computing Laboratory Department of Artificial Intelligence School of Informatics Xiamen361005 China Youtu Laboratory Tencent Shanghai200233 China National Tsing Hua University Department of Electrical Engineering The Institute of Communications Engineering Hsinchu30013 Taiwan Peng Cheng Laboratory Shenzhen518000 China Peking University Department of Computer Science and Technology Beijing100871 China School of Informatics and the Institute of Artificial Intelligence Xiamen University Media Analytics and Computing Laboratory Department of Artificial Intelligence Xiamen361005 China

Channel pruning has been long studied to compress convolutional neural networks (CNNs), which significantly reduces the overall computation. Prior works implement channel pruning in an unexplainable manner, which tends to reduce the final classification errors while failing to consider the internal influence of each channel. In this article, we conduct channel pruning in a white box. Through deep visualization of feature maps activated by different channels, we observe that different channels have a varying contribution to different categories in image classification. Inspired by this, we choose to preserve channels contributing to most categories. Specifically, to model the contribution of each channel to differentiating categories, we develop a class-wise mask for each channel, implemented in a dynamic training manner with respect to the input image's category. On the basis of the learned class-wise mask, we perform a global voting mechanism to remove channels with less category discrimination. Lastly, a fine-tuning process is conducted to recover the performance of the pruned model. To our best knowledge, it is the first time that CNN interpretability theory is considered to guide channel pruning. Extensive experiments on representative image classification tasks demonstrate the superiority of our White-Box over many state-of-the-arts (SOTAs). For instance, on CIFAR-10, it reduces 65.23% floating point operations per seconds (FLOPs) with even 0.62% accuracy improvement for ResNet-110. On ILSVRC-2012, White-Box achieves a 45.6% FLOP reduction with only a small loss of 0.83% in the top-1 accuracy for ResNet-50. Code is available at https://***/zyxxμWhite-Box. © 2012 IEEE.

关键词： Visualization

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Maximizing Profit of the Service Providers through Efficient Content Placement and Retrieval in an IoT-enabled Edge-Cloud Platform

Maximizing Profit of the Service Providers through Efficient...

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International Symposium on Advanced Networks and Telecommunication Systems (ANTS)

作者： Preet Gandhi Abhishriya Mahanta Manojit Ghose Aryabartta Sahu Department of Computer Science and Engineering Indian Institute of Technology Guwahati India School of Computing and Electrical Engineering Indian Institute of Technology Mandi India Department of Computer Science and Engineering Indian Institute of Information Technology Guwahati India

ISBN: (数字)9798350391725

ISBN: (纸本)9798350391732

In today’s 5G era, the amount of data generated by the Internet of Things (IoT) devices is enormous. data is processed and stored in the cloud under a traditional cloud computing architecture, and real-time processing and retrieval of these data is time-consuming. Therefore, an edge-cloud framework comes in handy where the content generated is placed on the edge nodes of the network, and it can reach the endusers in a time-bound manner. A majority of the previous research works on content placement under the edge-cloud architecture primarily aimed at improving the latency of the system. However, the approach proposed in this paper aims to maximize the profit of the service provider by effectively placing the content in a hierarchically organized edge-cloud network. We propose two strategies and compare them with random and greedy approaches for both synthetic and real data sets. The experimental results demonstrate the effectiveness of the proposed strategies, and the profit of the service providers increases by 38.81% (maximum) and 10.51% (on average) for the synthetic dataset and by 65.74% (maximum) and 22.83% (on average) for the real-world dataset when compared with the greedy approach.

关键词： Cloud computing 5G mobile communication computer architecture Real-time systems Telecommunications Internet of Things Synthetic data

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An Empirical Study of Domain Adaptation: Are We Really Learning Transferable Representations?

An Empirical Study of Domain Adaptation: Are We Really Learn...

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2022 IEEE International Conference on Big data, Big data 2022

作者： Josselyn, Nicholas Yin, Biao Zhang, Ziming Rundensteiner, Elke Department of Data Science Worcester Polytechnic Institute WorcesterMA United States Department of Computer Science Worcester Polytechnic Institute WorcesterMA United States Department of Electrical and Computer Engineering Worcester Polytechnic Institute WorcesterMA United States

ISBN: (纸本)9781665480451

Deep learning often relies on the availability of a large amount of high-quality labeled data, which can be very limited in novel domains. To address such data scarcity, domain adaptation is one promising approach that allows for deep networks to leverage large amounts of available data from a source domain to enhance the model's efficacy on the target domain of interest. However, while there is a plethora of alternate models for domain adaptation proposed over many years in the literature, there is a dearth of studies that objectively compare the relative effectiveness of these models in a rigorous, empirical study. To fill this gap, we provide a thorough, unbiased, empirical study of five state-of-the-art (SOTA) deep domain adaptation models proposed over the past 6 years whose codes are publicly available. Models are evaluated on the complex and diverse domain adaptation tasks featured in the DomainNet benchmark dataset as well as the popular Office-31 dataset. Our results suggest that (1) all 5 models perform similarly, on average, and do not even significantly beat the oldest model, and (2) counter to their intended purpose, the transfer loss functions in the literature do not contribute significantly to learning transferable representations. Our observations suggest that domain adaptation research needs to more thoroughly compare newly proposed models against existing works, along with assessing their loss functions' utility thoroughly. Our code and data splits are made public for reproducibility of results by the community. © 2022 IEEE.

关键词： Deep learning

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SF-DQN: Provable Knowledge Transfer using Successor Feature for Deep Reinforcement Learning 41

SF-DQN: Provable Knowledge Transfer using Successor Feature ...

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41st International Conference on Machine Learning, ICML 2024

作者： Zhang, Shuai Fernando, Heshan Devaka Liu, Miao Murugesan, Keerthiram Lu, Songtao Chen, Pin-Yu Chen, Tianyi Wang, Meng Department of Data Science New Jersey Institute of Technology NewarkNJ United States Department of Electrical Computer and Systems Engineering Rensselaer Polytechnic Institute TroyNY United States IBM Thomas J.Watson Research Center IBM Research Yorktown HeightsNY United States

This paper studies the transfer reinforcement learning (RL) problem where multiple RL problems have different reward functions but share the same underlying transition *** this setting, the Q-function of each RL problem (task) can be decomposed into a successor feature (SF) and a reward mapping: the former characterizes the transition dynamics, and the latter characterizes the task-specific reward *** Q-function decomposition, coupled with a policy improvement operator known as generalized policy improvement (GPI), reduces the sample complexity of finding the optimal Q-function, and thus the SF & GPI framework exhibits promising empirical performance compared to traditional RL methods like ***, its theoretical foundations remain largely unestablished, especially when learning the successor features using deep neural networks (SF-DQN).This paper studies the provable knowledge transfer using SFs-DQN in transfer RL *** establish the first convergence analysis with provable generalization guarantees for SF-DQN with *** theory reveals that SF-DQN with GPI outperforms conventional RL approaches, such as deep Q-network, in terms of both faster convergence rate and better *** experiments on real and synthetic RL tasks support the superior performance of SF-DQN & GPI, aligning with our theoretical findings. Copyright 2024 by the author(s)

关键词： Deep reinforcement learning

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SVM-Enhanced Dynamic Audiology Screening with IoT-Driven Monitoring for Personalized Hearing Health Management 2

SVM-Enhanced Dynamic Audiology Screening with IoT-Driven Mon...

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2nd International Conference on Self Sustainable Artificial Intelligence Systems, ICSSAS 2024

作者： Ronald, Bindu S. Raman, Ramakrishnan Vekariya, Daxa Baskar, V. Vijaya Rahul, J. Srinivasan, C. Symbiosis Law School Maharashtra Pune India Maharashtra Pune India Parul Institute of Engineering and Technology Parul University Faculty of Engineering and Technology Gujarat Vadodara India Sathyabama Institute of Science and Technology School of Electrical and Electronics Engineering Tamil Nadu Chennai India Agni College of Technology Department of Artificial Intelligence and Data Science Tamil Nadu Chennai India Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350368413

The increasing prevalence of hearing impairment across diverse age demographics, particularly in elderly populations, highlights the need for early diagnosis and ongoing therapy. Traditional audiology screening techniques often lack the necessary dynamic flexibility to meet personalized hearing health requirements over time. This system introduces an innovative method for dynamic audiology screening via IoT-based monitoring, using Support Vector Machine (SVM) algorithms for individualized hearing health management. The incorporation of IoT devices allows ongoing, real-time auditory data acquisition from the user's surroundings, recording essential metrics such as sound frequencies, noise levels, and auditory reactions. This data is analyzed using SVM to categorize and identify probable hearing impairments, providing great precision in differentiating between various degrees of hearing loss. The SVM model's strong classification skills render it especially effective in differentiating between varying levels of hearing loss, facilitating early identification and tailored therapy strategies. This tailored method of audiology screening boosts diagnostic precision and facilitates proactive management, perhaps improving long-term results for individuals with hearing impairments. © 2024 IEEE.

关键词： Electronic health record

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