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MediServe:An IoT-Enhanced Deep Learning Framework for Personalized Medication Management for Elderly Care

computers, Materials & Continua 2025年第4期83卷 935-976页

作者： Smita Kapse Ganesh Yenurkar Vincent Omollo Nyangaresi Gunjan Balpande Shravani Kale Manthan Jadhav Sahil Lawankar Vikrant Jaunjale Department of Computer Technology Yeshwantrao Chavan College of EngineeringWanadongariNagpur441110MaharashtraIndia Computer Science&Engineering Jaramogi Oginga Odinga University of Science&TechnologyBondo40601Kenya Department of Applied Electronics Saveetha School of EngineeringSIMATSChennai602105TamilnaduIndia

In today’s fast-paced world,many elderly individuals struggle to adhere to their medication schedules,especially those with memory-related conditions like Alzheimer’s disease,leading to serious health risks,hospital-izations,and increased healthcare *** reminder systems often fail due to a lack of personalization and real-time *** address this critical challenge,we introduce MediServe,an advanced IoT-enabled medication management system that seamlessly integrates deep learning techniques to provide a personalized,secure,and adaptive *** features a smart medication box equipped with biometric authentication,such as fingerprint recognition,ensuring authorized access to prescribed medication while preventing misuse.A user-friendly mobile application complements the system,offering real-time notifications,adherence tracking,and emergency alerts for caregivers and healthcare *** system employs predictive deep learning models,achieving an impressive classification accuracy of 98%,to analyze user behavior,detect anomalies in medication adherence,and optimize scheduling based on an individual’s habits and health ***,MediServe enhances accessibility by employing natural language processing(NLP)models for voice-activated interactions and text-to-speech capabilities,making it especially beneficial for visually impaired users and those with cognitive ***-based data analytics and wireless connectivity facilitate remote monitoring,ensuring that caregivers receive instant alerts in case of missed doses or medication ***,machine learning-based clustering and anomaly detection refine medication reminders by adapting to users’changing health *** combining IoT,deep learning,and advanced security protocols,MediServe delivers a comprehensive,intelligent,and inclusive solution for medication *** innovative approach not only improves the quality of life for elderly

关键词： MediServe medication health risks smart medication box

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NEMESIS: NORMALIZING THE SOFT-PROMPT VECTORS OF VISION-LANGUAGE MODELS 12

NEMESIS: NORMALIZING THE SOFT-PROMPT VECTORS OF VISION-LANGU...

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12th International Conference on Learning Representations, ICLR 2024

作者： Fu, Shuai Wang, Xiequn Huang, Qiushi Zhang, Yu Department of Computer Science and Engineering Southern University of Science and Technology China Computer Science Research Centre University of Surrey United Kingdom

With the prevalence of large-scale pretrained vision-language models (VLMs), such as CLIP, soft-prompt tuning has become a popular method for adapting these models to various downstream tasks. However, few works delve into the inherent properties of learnable soft-prompt vectors, specifically the impact of their norms to the performance of VLMs. This motivates us to pose an unexplored research question: "Do we need to normalize the soft prompts in VLMs?" To fill this research gap, we first uncover a phenomenon, called the Low-Norm Effect by performing extensive corruption experiments, suggesting that reducing the norms of certain learned prompts occasionally enhances the performance of VLMs, while increasing them often degrades it. To harness this effect, we propose a novel method named Normalizing the soft-prompt vectors of vision-language models (Nemesis) to normalize soft-prompt vectors in VLMs. To the best of our knowledge, our work is the first to systematically investigate the role of norms of soft-prompt vector in VLMs, offering valuable insights for future research in soft-prompt tuning. The code is available at https://***/ShyFoo/Nemesis. © 2024 12th International Conference on Learning Representations, ICLR 2024. All rights reserved.

关键词： Vectors

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CMCF-Net: An End-to-End Context Multiscale Cross-Fusion Network for Robust Copy-Move Forgery Detection

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IEEE Transactions on Multimedia 2024年 26卷 6090-6101页

作者： Xiong, Lizhi Xu, Jianhua Yang, Ching-Nung Zhang, Xinpeng Nanjing University of Information Science and Technology School of Computer Science Nanjing210044 China Jiangsu Collaborative Innovation Center on Atmospheric Environment and Equipment Technology Engineering Research Center of Digital Forensics Ministry of Education Nanjing210044 China Nanjing University of Information Science and Technology Software Engineering Nanjing210044 China National Dong Hwa University Department of Computer Science and Information Engineering Hualien974301 Taiwan Fudan University School of Computer Science Shanghai200433 China

Image copy-move forgery detection (CMFD) has become a challenging problem due to increasingly powerful editing software that makes forged images increasingly realistic. Existing algorithms that directly connect multiple scales of features in the encoder part may not effectively aggregate contextual information, resulting in poor performance. In this paper, an end-to-end context multiscale cross-fusion network (CMCF-Net) is proposed to detect image copy-move forgery. The proposed network consists of a multiscale feature extraction fusion (MSF) module and a multi-information fusion decoding (MFD) module. Multiscale information is efficiently extracted and fused in the MSF module utilizing stacked-scale feature fusion, which improves the network's forgery localization ability on objects of different scales. The MFD module employs contextual information combination and weighted fusion of multiscale information to guide the network in obtaining relevant clues from correlated information at multiple different scales. Experimental results and analysis have demonstrated that the proposed CMCF-Net achieves the best localization results with higher robustness. © 1999-2012 IEEE.

关键词： Feature extraction

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An Interactive Collaborative Creation System for Shadow Puppets Based on Smooth Generative Adversarial Networks

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computers, Materials & Continua 2024年第6期79卷 4107-4126页

作者： Cheng Yang Miaojia Lou Xiaoyu Chen Zixuan Ren Department of Industrial Design Hangzhou City UniversityHangzhou310000China College of Computer Science and Technology Zhejiang UniversityHangzhou310000China

Chinese shadow puppetry has been recognized as a world intangible cultural ***,it faces substantial challenges in its preservation and advancement due to the intricate and labor-intensive nature of crafting shadow *** ensure the inheritance and development of this cultural heritage,it is imperative to enable traditional art to flourish in the digital *** paper presents an Interactive Collaborative Creation System for shadow puppets,designed to facilitate the creation of high-quality shadow puppet images with greater *** system comprises four key functions:Image contour extraction,intelligent reference recommendation,generation network,and color adjustment,all aimed at assisting users in various aspects of the creative process,including drawing,inspiration,and content ***,we propose an enhanced algorithm called Smooth Generative Adversarial Networks(SmoothGAN),which exhibits more stable gradient training and a greater capacity for generating high-resolution shadow puppet ***,we have built a new dataset comprising high-quality shadow puppet images to train the shadow puppet generation *** qualitative and quantitative experimental results demonstrate that SmoothGAN significantly improves the quality of image generation,while our system efficiently assists users in creating high-quality shadow puppet images,with a SUS scale score of *** study provides a valuable theoretical and practical reference for the digital creation of shadow puppet art.

关键词： Shadow puppets deep learning image generation co-create

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Task Offloading and Resource Management for IIoT With Satellite–Terrestrial Integrated Computing Power Network Based on D3QN

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IEEE Internet of Things Journal 2025年第12期12卷 21770-21783页

作者： Li, Meng Li, Meihui Wang, Kan Yu, F. Richard Wang, Zhuwei Si, Pengbo Beijing University of Technology School of Information Science and Technology Beijing100124 China Xi’an University of Technology School of Computer Science and Engineering Xi’an710048 China Carleton University Department of Systems and Computer Engineering OttawaONK1S 5B6 Canada

The management of computing resources through the computing power network (CPN) has gradually become a focal point of research. With the development of the 6th generation (6G) mobile networks, some promising technologies, such as satellite-terrestrial integrated network (STIN) and smart endogenous network driven by artificial intelligence (AI) are increasingly being applied in Industrial Internet of Things (IIoT). However, several issues in current studies are worthy of attention: 1) the large number of devices powered by battery in IIoT;2) the complex communication environments;and 3) the finite computing resources for task data processing. To cope with these challenges, a satellite-terrestrial integrated CPN (STICPN) framework is introduced in this article. Within this framework, a task offloading link selection scheme is proposed, which minimizes the delay and the consumption of energy. The task offloading optimization problem is modeled as a markov decision process (MDP). Meanwhile, deep reinforcement learning (DRL) algorithm is employed to adapt to the dynamic states of environment. Specifically, a Dueling Double Deep Q Network (D3QN) is used to make optimal decisions and delay as well as energy consumption can be reduced significantly. Moreover, the D3QN-based scheme extends the usage time of IIoT devices. The simulation results indicate that the proposed scheme outperforms the comparison schemes significantly. © 2014 IEEE.

关键词： Energy utilization

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Securing the Internet of Things Through Device Identification via Network Traffic Analysis 10

Securing the Internet of Things Through Device Identificatio...

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10th International Conference on Signal Processing and Intelligent Systems, ICSPIS 2024

作者： Ahmadian, Rouhollah Ghatee, Mehdi Wahlstrom, Johan Amirkabir University of Technology Department of Computer Science Tehran Iran University of Exeter Department of Computer Science Exeter United Kingdom

ISBN: (纸本)9798331532543

The widespread adoption of the Internet of Things (IoT) has led to considerable security concerns, particularly in device identification. To tackle this challenge, we have devised a method that scrutinizes the data transmitted by IoT devices over the network, delving into detailed behavioral patterns that differentiate one device from another. Acknowledging the similarities in device behaviors, our approach concentrates on discerning and categorizing them based on their unique characteristics. This paper introduces an approach to identifying IoT devices by integrating probabilistic fusion and convolutional neural networks (CNNs). Our method involves segmenting input data into overlapping subsequences and using a CNN to independently classify each subsequence, enabling the detection of intricate patterns. The predictions are combined using an averaging method, leading to improvements. Specifically, the MFT dataset F1-score increased by 9.3%, the PC dataset by 2.46%, and the BMSAQ dataset by 24.6%. Empirical validation demonstrates that our model surpasses existing methods, offering a practical and efficient solution for real-world IoT applications. This research underscores the potential of advanced machine learning techniques in fortifying and safeguarding IoT ecosystems. © 2024 IEEE.

关键词： Convolutional neural networks

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A Hybrid Security Algorithm in Container-based Virtualization Environments Using Residue Number Systems 8

A Hybrid Security Algorithm in Container-based Virtualizatio...

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8th Conference on Information Communication technology and Society, ICTAS 2024

作者： Modey, Prince Freeman, Emmanuel Opoku-Yeboah, Stanley Avulekpor, Ebenezer Ghana Communication Technology University Department of Computer Science Accra Ghana University of Ghana Department of Computer Science Accra Ghana

ISBN: (纸本)9798350314892

The need for vigorous security methods becomes a great concern as container-based virtualization continues to gain close attention in modern edge computing environments. This study proposed a novel hybrid security algorithm designed specifically for containerized virtualization technology, leveraging the distinctive capabilities of Residue Number Systems (RNS). The proposed algorithm integrates the strengths of the bits permutation technique with the efficiency and parallelism inherent in RNS to enhance data security in containerized systems. The proposed algorithm combines RNS-based encryption and decryption processes with the bits permutation technique to form an adaptable security scheme. This proposed hybrid technique leverages an RNS's ability to perform modular arithmetic operations in parallel, offering data protection against certain attacks. The proposed security algorithm unlike the risk mitigation mechanisms like Linux-based, Firewall rules, MAC measures, and non-privileged mode, focused mainly on data protection in container-based virtualization environments. The algorithm's efficiency depends on the choice of moduli set and RNS reverse converter in terms of time and Hardware complexity. Furthermore, the proposed security algorithm was evaluated based on CRT with three moduli sets and a state-of-the-art RNS reverse converter in terms of time complexity. The proposed method performed better in terms of time complexity for the moduli set with a small dynamic range and the RNS reverse converter with less hardware complexity. © 2024 IEEE.

关键词： Containers

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Sufficient learning: mining denser high-quality pixel-level labels for edge detection

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Neural Computing and Applications 2025年第14期37卷 8245-8260页

作者： Chen, Xiaodiao Yang, Wenya Wu, Wen Tao, Xiuting Mao, Xiaoyang School of Computer Science and Technology Hangzhou Dianzi University Hangzhou310018 China School of Media and Design Hangzhou Dianzi University Hangzhou310018 China Department of Computer Science and Engineering University of Yamanashi Kofu400-8511 Japan

The success of deep neural networks can largely be attributed to large-scale datasets with accurate annotations. In many practical applications, labels are annotated by multiple annotators, resulting in ambiguous labels. To mitigate the impact of such labels, existing deep edge detectors tend only to exploit sparsely reliable samples with high-confident labels by simple voting mechanism. However, lacking sufficient pixel-level learnable samples would severely degrade detection performance. To combat this issue, this work designs a sufficient learning framework for edge detection (SLED) to sufficiently explore reliable samples for model learning, which consists of three stages: sample selection, label refinement, and retraining. Firstly, we propose a sample selection scheme founded on history prediction, which aims to identify more reliable samples and locate unreliable regions containing potentially reliable samples. Secondly, we design an efficient label refinement strategy based on a graph convolutional network (GCN) to propagate reliable label information to unreliable nodes. This strategy can generate "new training targets" with denser, reliable labels for all images. Finally, we retrain the network with these new targets, obtaining a more robust model. Experimental results on three widely used benchmarks show that the proposed detector outperforms all compared SOTA methods. Our code will be released at https://***/wenya1994/SLED. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Pixels

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Unleashing the Potential of Deep Learning for Precise Nuclei Segmentation and Classification in H &E-Stained Whole Slide Images 12th

Unleashing the Potential of Deep Learning for Precise Nuc...

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12th International Conference on Soft Computing for Problem Solving, SocProS 2023

作者： Majeed, Tabasum Aalam, Syed Wajid Ahanger, Abdul Basit Dar, Rayees Ahmad Masoodi, Tariq Ahmad Macha, Muzafar Ahmad Bhat, Ajaz A. Bhat, Muzafar Rasool Assad, Assif Department of Computer Science and Engineering Islamic University of Science and Technology Kashmir India Department of Computer Science Islamic University of Science and Technology Kashmir India Department of Computer Science and Engineering Islamic University of Science and Technology Jammu And Kashmir India Watson-Crick Centre for Molecular Medicine Islamic University of Science and Technology Kashmir India Department of Cancer Research Sidra Medicine Doha Qatar

ISBN: (纸本)9789819732913

Accurately identifying diverse cell types within and in the vicinity of the tumor matrix plays a crucial role in understanding the tumor microenvironment for cancer prognosis and scientific investigation. By automating the detection, segmentation, and classification of nuclei using Artificial Intelligence (AI), the burden on pathologists can be alleviated, minimizing errors caused by fatigue and subjectivity. In this study, we conducted a comprehensive evaluation of five pre-trained models: Patch DenseNet121, Patch EfficientNet-B3, Patch MobileNet, Patch VGG-16, Patch ResNet34, and a custom Patch Attention U-Net. These models offer an end-to-end solution for the automated segmentation and classification of nuclei from H &E-stained whole slide images of multiple organs (breast, kidney, lung, and prostate) using the MoNuSAC 2020 Challenge dataset. Performance was assessed using the F1-score and Intersection over Union (IoU) metrics. Among the models tested, Patch VGG-16 emerged as the top-performing model, achieving an impressive IoU of 85.48 and an F1-score of 0.88 on the validation dataset. The results of this study contribute to the advancement of AI-based techniques for the automated analysis of tumor microenvironments in diverse organ types, ultimately aiding in cancer diagnosis and treatment decision-making. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Tumors

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Adaptive Marine Predator Optimization Algorithm(AOMA)-Deep Supervised Learning Classification(DSLC)based IDS framework for MANET security

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Intelligent and Converged Networks 2024年第1期5卷 1-18页

作者： M.Sahaya Sheela A.Gnana Soundari Aditya Mudigonda C.Kalpana K.Suresh K.Somasundaram Yousef Farhaoui Department of Electronics and Communication Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and TechnologyChennai 600069India Department of Computer Science and Engineering Saveetha School of EngineeringSIMATSChennai 602105India JNIAS School of Planning and Architecture Hyderabad 500034India Department of Computer Science and Engineering NPR College of Engineering and TechnologyNatham Dindigul 624401India Department of Computer Science and Engineering PSNA College of Engineering and TechnologyPoolangulathupatti 620009India Department of Computer Science and Engineering Sri Muthukumaran Institute of TechnologyChennai 600069India Department of Computer Science Moulay Ismail UniversityMeknes 5003Morocco

Due to the dynamic nature and node mobility,assuring the security of Mobile Ad-hoc Networks(MANET)is one of the difficult and challenging tasks *** MANET,the Intrusion Detection System(IDS)is crucial because it aids in the identification and detection of malicious attacks that impair the network’s regular *** machine learning and deep learning methodologies are used for this purpose in the conventional works to ensure increased security of ***,it still has significant flaws,including increased algorithmic complexity,lower system performance,and a higher rate of ***,the goal of this paper is to create an intelligent IDS framework for significantly enhancing MANET security through the use of deep learning ***,the min-max normalization model is applied to preprocess the given cyber-attack datasets for normalizing the attributes or fields,which increases the overall intrusion detection performance of ***,a novel Adaptive Marine Predator Optimization Algorithm(AOMA)is implemented to choose the optimal features for improving the speed and intrusion detection performance of ***,the Deep Supervise Learning Classification(DSLC)mechanism is utilized to predict and categorize the type of intrusion based on proper learning and training *** evaluation,the performance and results of the proposed AOMA-DSLC based IDS methodology is validated and compared using various performance measures and benchmarking datasets.

关键词： Intrusion Detection System(IDS) Security Mobile Ad-hoc Network(MANET) min-max normalization Adaptive Marine Predator Optimization Algorithm(AOMA) Deep Supervise Learning Classification(DSLC)

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