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2025 International Conference on Pervasive Computational Technologies, ICPCT 2025

作者： Singh, Neelam Mittal, Varsha Rawat, Vandana Kumar, Priyansh Graphic Era Deemed to Be University Dept. of Computer Science and Engineering Uttrakhand Dehradun India Graphic Era Deemed to Be University Dept. of Computer Applications Uttrakhand Dehradun India

ISBN: (纸本)9798331508685

Both consumers and healthcare providers have benefited greatly from the quick development of software-based technology. The dominance of blockchains is impending as the medical care sector moves quickly towards digitization and customization. The development of blockchain provided answers to several problems that had long plagued the healthcare sector. However, not only in healthcare due to its capabilities, blockchain technology has found great success in a variety of industrial fields. The important factors propelling the growth of the healthcare blockchain market are the rise in data breaches, privacy concerns, fake pharmaceuticals, and provided answers to numerous problems that had long plagued the healthcare sector. Although telemedicine had been developing for a while, the covid-19 epidemic gave it the necessary kick. In order to facilitate social isolation, the pandemic made it necessary to expand virtual healthcare and distant therapy. Over the past year, the telemedicine market has experienced rapid expansion. In the current study, the adaptation issues and prospective prospects for blockchain technology in the telehealth and telemedicine sectors are *** on a systematic review, we have determined the existing state of the use of blockchain technology in a number of healthcare-related areas, the ideal state that would be achieved, the research gap regarding the usage of blockchain technology in a number of healthcare-related application areas, as well as a number of potential research agendas for future studies. © 2025 IEEE.

关键词： Telemedicine

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Drug Consumption Detection by Eye Using Image Processing Techniques

Drug Consumption Detection by Eye Using Image Processing Tec...

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2023 International Conference on computer Communication and Informatics, ICCCI 2023

作者： Pyingkodi, M. Thenmozhi, K. Wilfred Blessing, N.R. Archana, K. Arun Hiruthik, X. Dinesh Kumar, V. Kongu Engineering College Department of Computer Applications Erode India Kristu Jayanti College Department of Computer Science Bengaluru India University of Technology and Applied Sciences-Ibri Department of IT Oman Kongu Engineering College Computer Applications Erode India

ISBN: (纸本)9798350348217

Drug usage is a significant global economic issue that results in countless losses. This study suggests a convolutional neural network-based image processing method for identifying drugged eyes. The front-end packages already in use for this article collect information from eye images using the CNN algorithm. However, it can take some time. As a result, the suggested approach may be used to swiftly and automatically identify drugged eyes. The acquisition of the input image, picture pre-processing, and image processing are the main processes in the suggested *** the reddish regions, emphasising the afflicted regions, confirming the training set, and presenting the findings. Some eyes may go unnoticed, such those that have been socially poisoned. This technique was tried on drugged eyes at different phases, both with and without drug consumption. The technique was used to find a white region of the eye in a given input picture. Images of both drugged and unaffected eyes were presented for training purposes. In order to choose the optimum colour model for this strategy, photos were first transformed to colour models. The Support Erosion technique and the Local Binary Pattern were both used in the model construction for feature extraction. This technique can detect drugged eyes with an average accuracy of 95%. © 2023 IEEE.

关键词： Local binary pattern

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Soil Analysis Using Deep Learning for Precision Agriculture 1

Soil Analysis Using Deep Learning for Precision Agriculture

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1st International Conference on Pioneering Developments in computer science and Digital Technologies, IC2SDT 2024

作者： Fotabong, Thierry Forsack Muhammad, Abdullahi Keisham, Murphy Mehrotra, Tushar Singh, Rajneesh Kumar Singh, S. Pratap Agrawal, Arun Prakash Sharda School of Engineering and Technology Sharda University Department of Computer Science and Applications Greater Noida India School of Computing Science and Engineering Galgotias University Department of Computer Science and Engineering Greater Noida India Symbiosis Institute of Technology Nagpur Campus Deemed University Pune India

ISBN: (纸本)9798350365016

This research explores the application of deep learning techniques for soil analysis in precision agriculture, focusing on the classification of soil types using convolutional neural networks (CNNs). The study addresses the limitations of traditional soil classification methods, which are often labor-intensive and reliant on expert judgment. The proposed approach involves collecting soil image data, preprocessing it using techniques such as Gabor filtering, and classifying the images with a CNN model. The methodology includes data acquisition, feature extraction, and model training, aiming to accurately predict soil parameters using diverse input data sources like sensor readings and satellite imagery. The potential benefits of this approach include real-time monitoring, optimal fertilizer application, and enhanced crop management, which can contribute to sustainable agricultural practices and food security. © 2024 IEEE.

关键词： Convolutional neural networks

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Empowering Dynamic Scheduling in IoT Networks through Collaborative Policy Learning via Federated Reinforcement Techniques 2

Empowering Dynamic Scheduling in IoT Networks through Collab...

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2nd IEEE International Conference on Advances in Information Technology, ICAIT 2024

作者： Thirunavukkarasu, M. Thaiyalnayaki, S. Jose, Naduvathezhath Nessariose Maheshwari, S. Pavithra, B. Malathi, G. Department of Computer Science & Applications S.A College of Arts & Science India Department of Computer Science Rabiammal Ahamed Maideen College for Women India Denken Solutions India Department of Information Technology Sri Sai Ranganathan Engineering College India Department of Computer science and Engineering Sri Sai Ranganathan Engineering College India Department of Computer Science and Engineering Sri Sai Ram Institute of Technology Chennai India

ISBN: (纸本)9798350383867

In the burgeoning landscape of Internet of Things (IoT) networks, efficient management of resources is paramount for ensuring optimal performance and resource utilization. Dynamic scheduling, particularly in the context of cloud-edge-terminal IoT networks, presents a significant challenge due to the diverse and dynamic nature of connected devices and their varying computational requirements. Traditional centralized approaches to scheduling may prove inadequate in such dynamic environments, necessitating the exploration of novel techniques. This project proposes a pioneering approach to address the dynamic scheduling challenges in IoT networks by leveraging collaborative policy learning through federated reinforcement techniques. The proposed framework harnesses the power of federated learning, a decentralized machine learning paradigm, to collectively train policies for dynamic scheduling tasks across distributed edge and terminal devices while preserving data privacy and security. Key components of the proposed framework include a collaborative learning architecture that orchestrates the exchange of policy updates among edge and terminal devices, enabling them to adaptively refine their scheduling policies based on local observations and feedback. Reinforcement learning serves as the underlying mechanism for policy optimization, allowing devices to learn and adapt to evolving network conditions and user demands over time. By decentralizing the learning process and leveraging the collective intelligence of edge and terminal devices, the proposed framework offers several advantages. These include improved scalability, reduced communication overhead, and enhanced resilience to network failures. Furthermore, the federated approach ensures data privacy and regulatory compliance by keeping sensitive information localized to individual devices. To evaluate the effectiveness of the proposed framework, comprehensive simulations and real-world experiments will be conducted u

关键词： Reinforcement learning

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A Semi-supervised Approach of Cluster-Based Topic Modeling for Effective Tweet Hashtag Recommendation

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SN computer science 2024年第7期5卷 1-14页

作者： Pattanayak, Pradipta Kumar Tripathy, Rudra M. Padhy, Sudarsan Research Scholar BPUT(Rourkela) Department of Computer Science and Engineering Silicon University Patia Odisha Bhubaneswar 751024 India School of Computer Science and Engineering XIM University Harirajpur Odisha Bhubaneswar 752050 India Institute of Mathematics and Applications Andharua Odisha Bhubaneswar 751029 India

Twitter has emerged as a significant source of data to be used for text summarization, Topic Modeling, document clustering, information retrieval, sentiment analysis, etc. Using hashtags, Twitter users may categorize their tweets as hashtags provide the essential meta-information in connecting tweets to the underlying themes. However, the majority of tweets do not have hashtags, which makes it challenging to search for a particular theme. The proposed model is designed to recommend appropriate hashtags for tweets by considering their categorization into sports, politics, health, or technology. In this paper, we proposed a novel heuristic for recommending hashtags of tweets. Taking 20,000 tweets, which includes 5000 tweets from each of the specified four topics along with their respective hashtags. These hashtags were manually assigned by a group of experts, which were subsequently excluded during the topic modeling process. The basic data-cleaning technique is applied to clean and tokenize the tweets. Then Word2Vec technique is used to vectorize the tokens which captured the semantic meaning of the words in the tweets and overcomes the data sparsity issues. The dimension of the data is reduced using Singular Value Decomposition (SVD) followed by t-SNE (t-distributed Stochastic Neighbor Embedding). The reduced data is divided into four clusters and a semi-supervised method is introduced to link these clusters to the aforementioned topics, which eventually helped to produce the hashtag for a list of tweets. On comparison of our results with existing techniques, it is observed that our model performance is better with respect to the metrics: precision, recall, and F1-score. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Gibbs sampling Dirichlet mixture model Linear Dirchlet allocation Singular value decomposition Topic modeling Word2Vec

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Deep Learning Techniques for Object Detection in Underwater Environments

Deep Learning Techniques for Object Detection in Underwater ...

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

作者： Praveen, R.V.S. Maindola, Meenakshi Anandhi, R.J. Thandra, Nagarjuna Bansal, Saloni Sharma, Vishal LTIMindtree Limited Telangana Hyderabad India Graphic Era Deemed to Be University Department of Computer Science & Engineering Dehradun India New Horizon College of Engineering Department of Information Science Engineering Bangalore India MLR Institute of Technology Dept. Computer Science and Engineering Telangana Hyderabad India GLA University Department of Computer Engineering and Applications Mathura India Lovely Professional University Phagwara India

ISBN: (纸本)9798331512965

The degradation of image quality caused by things like light absorption, scattering, and distortion makes object detection in underwater environments a unique challenge. applications like marine research, environmental monitoring, and autonomous underwater vehicles (AUVs) are restricted in their effectiveness by traditional image processing techniques, which often struggle with these complexities. Recent years have seen the rise of deep learning techniques as a potent weapon in the fight against these obstacles. In order to accurately detect objects underwater, this paper investigates the use of deep learning models, specifically convolutional neural networks (CNNs). We explore cutting-edge models and methods like attention mechanisms, transfer learning, and hybrid deep learning architectures to improve detection performance in challenging underwater environments. Furthermore, we utilize data augmentation techniques and synthetic dataset generation to tackle the critical issue of limited labeled underwater datasets. From a detection accuracy and robustness perspective, the experimental results show that our suggested deep learning method is light years ahead of the competition. Marine exploration, autonomous underwater vehicle navigation, and underwater surveillance systems can all benefit from this study's new insights into how to better detect objects in real time while submerged. © 2025 IEEE.

关键词： Convolutional neural networks

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Designing an Intelligent Model that Employs Advanced Machine Learning Techniques to Optimize Disease Prediction and Classification in Humans 1

Designing an Intelligent Model that Employs Advanced Machine...

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1st International Conference on Trends in engineering Systems and Technologies, ICTEST 2024

作者： Kumari, Sudesh Lekshmi, V. Arathi Chandran, R.I. Shankar, Jaya Vishnu Priya, P.M. Bps Women University Department of Electronics and Communication Engineering Sonipat India Christ Nagar College Department of Computer Science and Applications Kerala Maranalloor India Sree Narayana Institute of Technology Department of Computer Science and Engineering Kerala Adoor India Department of Computer Science Karnataka Bengaluru India

ISBN: (纸本)9798350307795

Machine learning (ML) has emerged as a powerful tool with applications across diverse fields, including clinical diagnostics. With the growing prevalence of heart and liver diseases worldwide, early detection is paramount for saving lives. This study presents a hybrid ML model that combines the support vector machine (SVM) approach with a modified particle swarm optimization (PSO) model for the classification of heart and liver disease data. Utilizing datasets from the UCI ML repository, we evaluate the model's performance using various metrics, including categorization accuracy, error rates, correctness, recall, and the F1 score. Comparative analyses with traditional SVM and other hybrid algorithms such as the PSOSVM and Crazy PSO-SVM (CPSOSVM) are conducted. The experiments encompass the prediction of cardiac and liver illness. Results demonstrate that the designed Cauchy Crazy PSOSVM (CCPSOSVM) algorithm outperforms other approaches, achieving the greatest accuracy rate and least error rate. While this study focuses on cardiac and hepatic illness prediction, the CCPSOSVM algorithm holds promise for the extension of disease prediction across various medical domains. © 2024 IEEE.

关键词： Support vector machines

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A holistic securing approach to speech steganography system with message embedding and pause optimization

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Multimedia Tools and applications 2025年 1-27页

作者： Kumar, Surendra Yadav, Arun Singh Dwivedi, Mridula Kumar, Mohit Department of Computer Engineering and Applications GLA University Uttar Pradesh Mathura281401 India Department of Computer Science University of Lucknow Uttar Pradesh Lucknow226007 India Lucknow India Department of IT Dr. B.R. Ambedkar National Institute of Technology Jalandhar India

Nowadays, prevention control of message sharing as well as security enhancement in speech transmission which occurs due to users having a passion for novel inventions, communication system benefits. In this paper, the main objective is to design a speech steganography framework with covert message data transmission including speech. The secret message data is encrypted with the help of the Hyperelliptic Curve Cryptography (HECC) algorithm to provide a maximum security environment. The designed speech steganography framework is integrated with the pause removal mechanism respectively. Since, the Stegospeech is sent into the channel, as the Stegospeech as of now obtained the secret Message data its transfer speed will increment. Then at that point, it needs high bandwidth for transmission as well as needs higher energy utilization. In this manner, to decrease the data transfer speed, the best procedure is to eliminate the undesirable pause in the speech, the pause removal strategy carried out utilizing the combinations of Ensemble Empirical Mode Decomposition (EEMD) with Hilbert-Huang Transform (HHT). Then, some techniques are used to embed the message into the speech like 4-level Discrete Wavelet Transformation (DWT), Matrix definition, and Single Value Decomposition (SVD). They are likewise utilized for the secret message extraction from speech signal as well as message information from the Stegospeech signal. The presented strategy is carried out and analyzed towards the different invasions in the output. The Correlation Coefficient (CC), the Peak Signal-To-Noise Ratio (PSNR), the Signal-To-Noise Ratio (SNR), the Number of Pixels Changing Rate (NPCR), and the Unified Average Changing Intensity (UACI) are some of the popular standards that are utilised when comparing two encrypted voice signals. The numbers 33.3% and 100%, respectively, for UACI and NPCR are considered to be optimum states. It is noticed that the presented technique provides better Statistical resul

关键词： Steganography

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A Blockchain assisted fog computing framework for secure data sharing mechanism in smart home 1

A Blockchain assisted fog computing framework for secure dat...

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1st IEEE International Conference on Contemporary Computing and Communications, InC4 2023

作者： Apat, Hemant K. Sahoo, Bibhudatta Mohanty, Sagarika Mukul, Manas Barik, Rabindra K. National Institute of Technology Computer Science and Engineering Rourkela India Kalinga Institute of Industrial Technology School of Computer Applications Bhubaneswar India

ISBN: (纸本)9798350335774

The demand and popularity of Internet of Things (IoT) has been growing with exponential rate due to the rapid growth of various smart devices in the IoT ecosystem. The request generated through various smart devices is generally stochastic and unscheduled. The existing IoT-Cloud paradigm does not provide the security services efficiently and effectively. In order to provide better security services to different IoT applications domain particularly in smart home domain, it introduces the blockchain technology. So, in the present article, it proposes BlockCFS framework, which is an innovative blockchain assisted fog computing framework, as a safe datasharing mechanism for smart homes. It also illustrates the methodical sequence diagram for the BlockCFS, for registering and transmitting data from a variety of smart devices that are connected in a smart home. © 2023 IEEE.

关键词： Fog computing

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Deep Reinforcement Learning Based Secure Transmission for UAV-Assisted Mobile Edge Computing

International Journal of Interactive Mobile Technologies

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International Journal of Interactive Mobile Technologies 2024年第17期18卷 154-169页

作者： Vijayalakshmi, N. Gulati, Sagar Sujin, B. Ben Rao, B. Madhav Kumar, K. Kiran Department of Computer Science and Applications SRMIST Ramapuram Campus Tamil Nadu Chennai India School of Computer Science and IT JAIN Deemed to be University Karnataka Bengaluru India Computer Engineering Department University of Technology and Applied Sciences Nizwa Oman Department of CSE SIR C R Reddy College of Engineering Andhra Pradesh Eluru India Department of CSE Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram India

The increasing computational demand for real-time mobile applications has led to the development of mobile edge computing (MEC), with support from unmanned aerial vehicles (UAVs), as a promising paradigm for constructing high-throughput line-of-sight links for ground users and pushing computational resources to network edges. Users can reduce processing latency and the load on their local computers by delegating tasks to the UAV in its role as an edge server. The coverage capacity of a single UAV is, however, very limited. Moreover, it will be easy to intercept the data that is transferred to the unmanned aerial vehicle. Thus, for UAV-assisted mobile edge computing, we proposed a transmission technique based on multi-agent deep reinforcement learning in this study. The recommended approach to maximize UAV deployment first applies the particle swarm optimization algorithm. Then, deep reinforcement learning is utilized to optimize the secure offloading to maximize the system utility and minimize the quantity of information eavesdropping, taking into consideration different user task types with diverse preferences for processing time and residual energy of computing equipment. The results of the simulation demonstrate that, in comparison to the single-agent strategy and the benchmark, the multi-agent approach can optimize offloading more successfully and produce higher system utility. © 2024 by the authors of this article.

关键词： Deep reinforcement learning

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