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A novel medical image data protection scheme for smart healthcare system

CAAI Transactions on Intelligence technology 2024年第4期9卷 821-836页

作者： Mujeeb Ur Rehman Arslan Shafique Muhammad Shahbaz Khan Maha Driss Wadii Boulila Yazeed Yasin Ghadi Suresh Babu Changalasetty Majed Alhaisoni Jawad Ahmad School of Computer Science and Informatics Cyber Technology InstituteDe Montfort UniversityLeicesterUK Department of Biomedical Engineering University of GlasgowGlasgowUK School of Computing Engineering and the Built EnvironmentEdinburgh Napier UniversityEdinburghUK Robotics and Internet‐of‐Things Laboratory Prince Sultan UniversityRiyadhSaudi Arabia Department of Computer Science Al Ain UniversityAl AinUnited Arab Emirates Department of Computer Engineering College of Computer Science King Khalid UniversityAbhaSaudi Arabia Computer Sciences Department College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadhSaudi Arabia

The Internet of Multimedia Things(IoMT)refers to a network of interconnected multimedia devices that communicate with each other over the ***,smart healthcare has emerged as a significant application of the IoMT,particularly in the context of knowledge‐based learning *** healthcare systems leverage knowledge‐based learning to become more context‐aware,adaptable,and auditable while maintain-ing the ability to learn from historical *** smart healthcare systems,devices capture images,such as X‐rays,Magnetic Resonance *** security and integrity of these images are crucial for the databases used in knowledge‐based learning systems to foster structured decision‐making and enhance the learning abilities of ***,in knowledge‐driven systems,the storage and transmission of HD medical images exert a burden on the limited bandwidth of the communication channel,leading to data trans-mission *** address the security and latency concerns,this paper presents a lightweight medical image encryption scheme utilising bit‐plane decomposition and chaos *** results of the experiment yield entropy,energy,and correlation values of 7.999,0.0156,and 0.0001,*** validates the effectiveness of the encryption system proposed in this paper,which offers high‐quality encryption,a large key space,key sensitivity,and resistance to statistical attacks.

关键词： data analysis medical image processing security

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A hybrid convolutional neural network for sarcasm detection from multilingual social media posts

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Multimedia Tools and Applications 2025年第16期84卷 15867-15895页

作者： Pandey, Rajnish Kumar, Abhinav Singh, Jyoti Prakash Tripathi, Sudhakar Department of Information Technology IET Dr Rammanohar Lohia Awadh University Ayodhya Uttar Pradesh Faizabad India Department of Computer Science and Engineering Motilal Nehru National Institute of Technology Allahabad Prayagraj211004 India Department of Computer Science and Engineering National Institute of Technology Patna Patna India Department of Information Technology Rajkiya Engineering College Uttar Pradesh Ambedkarnagar India

Sarcasm in social media postings significantly impacts automated sentiment extraction due to its potential to invert the overall polarity of phrases. It poses a formidable challenge in extracting genuine sentiments from informal multilingual comments about events or individuals, especially when the message is in a multilingual form. The prevalence of noisy datasets further complicates this task. This work proposes a hybrid convolutional neural network (CNN-H) model to classify sarcastic and non-sarcastic statements. Unlike traditional CNN models, CNN-H integrates character and word embeddings, enhancing its capacity to handle the complexities of multilingual content. Evaluation on five benchmark datasets, including English (news headlines, Ghosh Tweet, Riloff Tweet), Hindi, and multilingual Hindi-English (Hinglish), demonstrates the superior performance of CNN-H. The model surpasses various state-of-the-art conventional machine-learning and deep-learning models, including CNN, achieving an impressive F1-score of 93.0% on the multilingual dataset. The incorporation of character and word embeddings proves pivotal in mitigating the impact of noisy datasets, prevalent in social media posts. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Sentiment analysis

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Machine learning approach for data analysis and predicting coronavirus using COVID-19 India dataset

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International Journal of Business Intelligence and Data Mining 2023年第1期24卷 47-73页

作者： Singh, Soni Ramkumar, K.R. Kukkar, Ashima Department of Computer Science and Engineering Chitkara Institute of Engineering and Technology Chitkara University Punjab India

According to the World Health Organisation (WHO), the COVID-19 virus would infect 83,558,756 persons worldwide in 2020, resulting in 646,949 deaths. In this research, we aim to find the link between the time series data and current circumstances to predict the future outbreak and try to figure out which technique is best for modelling for accurate predictions. The performance of different machine learning (ML) models such as sigmoid function, Facebook (FB) prophet model, seasonal auto-regressive integrated moving average with eXogenous factors (SARIMAX) model, support vector machine (SVM) learning model, linear regression (LR) model, and polynomial regression (PR) model are analysed along with their error rate. A comparison is also done to evaluate a best-suited model for prediction based on different categorisation approaches on the WHO authenticated dataset of India. The result states that the PR model shows the best performance with time-series data of COVID-19 whereas the sigmoid model has the consistently smallest prediction error rates for tracking the dynamics of incidents. In contrast, the PR model provided the most realistic prediction to identify a plateau point in the incident’s growth curve. Copyright © 2024 Inderscience Enterprises Ltd.

关键词： COVID-19

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Enhancing Crop Health Monitoring: A ResNet50 Approach to Automated Plant Disease Severity Prediction

Enhancing Crop Health Monitoring: A ResNet50 Approach to Aut...

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

作者： Gunavathi, H.S. Priya, Ishani Sree, Kothakota Bindhu Kushwaha, Aparna Bangalore Institute of Technology Department of Computer Science & Engineering India

ISBN: (纸本)9798331529833

Plant diseases significantly impact agricultural productivity, making it essential to accurately assess not only the presence of disease but also its severity. This paper presents a deep learning-based framework using ResNet50 to predict plant disease severity across multiple levels (Healthy, Mild, Moderate, and Severe). The system is designed to process plant leaf images, perform augmentation to improve robustness and classify disease severity with high accuracy. The proposed method leverages labelled and pseudo- labelled datasets to enhance the model's performance through data augmentation and training on unlabelled images. The results demonstrate the model's effectiveness in distinguishing between varying levels of disease progression, providing valuable insights for early intervention and optimized crop management strategies. This platform is crucial for automating plant disease severity monitoring, and aiding decision-making processes for timely interventions in agriculture. © 2025 IEEE.

关键词： agriculture data augmentation deep learning image classification plant disease ResNet50 severity prediction

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Comprehensive insights into healthcare IoT: the role of machine learning and deep learning approaches

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Multimedia Tools and Applications 2025年 1-42页

作者： Kumar, Atul Dewang, Rupesh Kumar Computer Science and Engineering Department Motilal Nehru National Institute of Technology Allahabad Uttar Pradesh Prayagraj211004 India

Conventional healthcare systems have long struggled to address the varied needs of large patient populations, often leading to inefficiencies and less-than-optimal outcomes. Yet, the advent of machine learning (ML) and deep learning (DL) models has heralded a transformative shift toward value-based care, enabling healthcare providers to offer personalized and remarkably effective treatments. Contemporary medical equipment and devices are equipped with internal applications that gather and store extensive patient data, forming a valuable resource for ML-powered predictive *** have conducted a systematic literature review (SLR) which mainly focuses on Theoretical background, challenges, Application framework, Organization of IoT in healthcare and HIoT *** the SLR process some efficient methodologies has been used. In the SLR process we have conducted Quality metadata analysis (QMA) which provides the complete statistical analysis by considering different *** learning algorithms enable the analysis of the extensive data produced by IoT devices, uncovering patterns and irregularities that may escape human detection. Deep learning, leveraging sophisticated neural network models, enhances the accuracy of medical image interpretation, disease prognosis, and forecasting patient outcomes. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Diagnosis

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Real-Time Health Monitoring System by Integrating Augmented Reality and Internet of Things 6th

Real-Time Health Monitoring System by Integrating Augmented ...

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6th International Conference on Intelligent Communication, Control and Devices, ICICCD 2024

作者： Kulkarni, Radhika V. Shilimkar, Avanish Bhegade, Sakshi Dadmal, Jidnyasa Department of Computer Engineering Vishwakarma Institute of Technology Pune India

ISBN: (纸本)9789819783281

In order to provide real-time health monitoring in the healthcare industry, the article presents a novel integration of augmented reality (AR) and the Internet of Things (IoT). Our goal is to build a unified system that combines IoT and AR technologies to produce an interesting health monitoring service. Our study reveals the combined potential of these technologies, which have been explored separately in previous research. We close the gap in the literature by utilising AR for vital indicator monitoring and demonstration, which allows for accurate real-time patient monitoring. Our system architecture incorporates Blynk Cloud for data storage, Unity 3D for complex augmented reality health data visualisation, MQTT protocol for secure data sharing, and sensors connected to ESP32s. Our method, which improves medical education and technology integration, lays the groundwork for a revolutionary change in healthcare by utilising ARKit, ARCore, Vuforia, and OpenCV. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Electronic health record

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Contemporary Approaches to Analyze Non-stationary Time-series: Some Solutions and Challenges

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Recent Advances in computer Science and Communications 2023年第2期16卷 61-80页

作者： Dixit, Ankit Jain, Shikha Department of Computer Science & Engineering and Information Technology Jaypee Institute of Information Technology Noida India

Enhancement of technology yields more complex time-dependent outcomes for better understanding and analysis. These outcomes generate more complex, unstable, and high-dimensional data from non-stationary environments. Hence, more challenges are arising day by day to fulfill the increasing demand for future estimation. Thus, in this paper, an extensive study has been presented to comprehend the statistical complexity and randomness of Non-Stationary Time Series (NS-TS) data at the atomic level. This survey briefly explains the basic principles and terms related to Non-Stationary Time Series (NS-TS). After understanding the fundamentals of NS-TS, this survey categorized non-stationary time series into groups and subgroups based on a change in statistical behavior. It also provides a comprehensive discussion on contemporary approaches proposed by researchers in each category of non-stationarity. These algorithms include clustering, classification, and regression techniques to deal with different types of domains. Every category of non-stationarity consists of a separate table to draw some advantages and disadvantages of existing approaches. At the end of each non-stationarity type, a short discussion and critical analysis have been done. In the conclusion section, it is observed that this research sphere still has many open challenges that need to be addressed and demand more exploration. Furthermore, it discusses the possible solution of improvisation in future research. © 2023 Bentham Science Publishers.

关键词： Time series

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Efficient resource allocation for D2D-enabled social IoT networks:A tripartite and time-scale optimization approach

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Intelligent and Converged Networks 2024年第4期5卷 380-401页

作者： Saurabh Chandra Rajeev Arya Maheshwari Prasad Singh Department of Electronics&Communication Engineering National Institute of Technology PatnaPatna 800005India Department of Computer Science and Engineering National Institute of Technology PatnaPatna 800005India

In the densification of Device-to-Device(D2D)-enabled Social Internet of Things(SIoT)networks,improper allocation of resources can lead to high interference,increased signaling overhead,latency,and disruption of Channel State Information(CSI).In this paper,we formulate the problem of sum throughput maximization as a Mixed Integer Non-Linear Programming(MINLP)*** problem is solved in two stages:a tripartite graph-based resource allocation stage and a time-scale optimization *** proposed approach prioritizes maintaining Quality of Service(QoS)and resource allocation to minimize power consumption while maximizing sum *** results demonstrate the superiority of the proposed algorithm over standard benchmark *** of the proposed algorithm using performance parameters such as sum throughput shows improvements ranging from 17%to 93%.Additionally,the average time to deliver resources to CSI users is minimized by 60.83%through optimal power *** approach ensures QoS requirements are met,reduces system signaling overhead,and significantly increases D2D sum throughput compared to the state-of-the-art *** proposed methodology may be well-suited to address the challenges SIoT applications,such as home automation and higher education systems.

关键词： Device-to-Device(D2D)communication Social Internet of Things(SIoT) resource allocation Quality of Service(QoS) tripartite graph time scale

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Leveraging Network Slicing in SDNs for Handling Application Failures

IAENG International Journal of Computer Science

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IAENG International Journal of computer Science 2024年第4期51卷 409-421页

作者： Lokesh, Bommareddy Rajagopalan, Narendran School of Computer Science and Engineering VIT-AP University Andhra Pradesh Amaravati522237 India Department of Computer Science and Engineering National Institute of Technology Puducherry Karaikal609609 India

Unlike traditional networks, Software-defined networks (SDNs) provide an overall view and centralized control of all the devices in the network. SDNs enable the network administrator to implement the network policy by programming applications on top of the SDN controller using generic APIs. One or more controller instances can be deployed to administer the data flow by maintaining unified control of the entire network. The controller is expected to respond quickly to queries from forwarding devices. Presuming quick responses from the controller while enforcing a complex security mechanism is unreasonable. In this paper, the authors propose a unique, adaptive, lightweight, yet efficient technique called ISOLATOR to mitigate the effect of insider attacks and malfunctioning of distributed applications in an SDN-enabled cloud. The proposed security application upon detecting any suspicious activity by a virtual machine, isolates it by removing the interface to its respective shared network and reconnects it through a restricted network operating in a highly selective mode. By subjecting the data traffic to deep packet inspection, the restricted network searches for a match with a known worm pattern. The application is programmed for the OpenDayLight controller and the results show a significant improvement in identifying malicious activities with minimal latency and computational cost. © (2024), (International Association of Engineers). All Rights Reserved.

关键词： Controllers

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Smart City: Practice of Artificial Intelligence, Robotics and Internet of Things

Smart City: Practice of Artificial Intelligence, Robotics an...

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2024 International Conference on Computational Intelligence and Computing Applications, ICCICA 2024

作者： Gupta, Suresh Chand Kumar, Deepak Parkash, Ved Bindra, Sandeep Singh Panipat Institute Of Engineering And Technology Department Of Computer Science & Engineering Haryana Panipat India State Institute Of Engineering And Technology Department Of Computer Engineering Haryana Nilokheri India

ISBN: (纸本)9798350306446

The discussion about smart cities under review about for a long while in established researchers, enterprises, and cultural gatherings, and various audits on the point are available. The term "smart city"alludes to new businesses using data and correspondence advancements alongside the capacities and conditions of metropolitan areas. Discussing about smart city thoughts and analyses towards certifiable acknowledgment, while empowering the mentioned attributes, is a momentum contest. The further empowering of broad smart city facilities and their combination need financially and monetarily solid executions. This will enhance the demands on the designing, sending, activity, upkeep, and development of smart cities. © 2024 IEEE.

关键词： Smart city

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