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A Secure and Efficient information Authentication Scheme for E-Healthcare System

computers, Materials & Continua 2023年第9期76卷 3877-3896页

作者： Naveed Khan Jianbiao Zhang Ghulam Ali Mallah Shehzad Ashraf Chaudhry Faculty of Information Technology Beijing University of TechnologyBeijing100124China Department of Computer Science Shah Abdul Latif UniversityKhairpur66111Pakistan Department of Computer Engineering Faculty of Engineering ArchitectureNisantasi UniversityIstanbul34398Turkey

The mobile cellular network provides internet connectivity for heterogeneous Internet of Things(IoT)*** cellular network consists of several towers installed at appropriate locations within a smart *** cellular towers can be utilized for various tasks,such as e-healthcare systems,smart city surveillance,traffic monitoring,infrastructure surveillance,or sidewalk *** is a primary concern in data broadcasting,particularly authentication,because the strength of a cellular network’s signal is much higher frequency than the associated one,and their frequencies can sometimes be aligned,posing a significant *** a result,that requires attention,and without information authentication,such a barrier cannot be ***,we design a secure and efficient information authentication scheme for IoT-enabled devices tomitigate the flaws in the e-healthcare *** proposed protocol security shall check formally using the Real-or-Random(ROR)model,simulated using ProVerif2.03,and informally using pragmatic *** comparison,the performance phenomenon shall tackle by the already result available in the MIRACL cryptographic lab.

关键词： IoT-enable device e-healthcare authentication edge computing

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Exploring the Potential of Deep Learning Algorithms in Medical Image Processing: A Comprehensive Analysis 5th

Exploring the Potential of Deep Learning Algorithms in Medic...

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5th International Conference on Communication and Intelligent Systems, ICCIS 2023

作者： Pal, Ganesh Prasad Pal, Raju Department of Computer Science and Engineering Jaypee Institute of Information Technology Noida India Department of Computer Science and Engineering School of Information and Communication Technology Gautam Buddha University Greater Noida India

ISBN: (纸本)9789819720781

In this study, we comprehensively examine the potential of deep learning algorithms in the domain of medical image processing. Through a systematic analysis of existing literature, we explore the applications, methodologies, and outcomes of these algorithms across diverse medical specialties. Our analysis underscores the transformative impact of convolutional neural networks and transfer learning techniques in enhancing diagnostic accuracy, image segmentation, and disease detection. We discuss challenges such as data variability, ethics, and interpretability while emphasizing the importance of interdisciplinary collaboration. By synthesizing findings and identifying future directions, we highlight the promising role of deep learning in revolutionizing healthcare diagnostics and treatment planning. After conducting a thorough analysis of various literature and empirical studies, we have evaluated the capabilities and drawbacks of deep learning models in managing different medical imaging modalities such as X-rays, MRIs, and CT scans. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： COVID-19

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DNA Computing with Water Strider Based Vector Quantization for Data Storage Systems

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computers, Materials & Continua 2023年第3期74卷 6429-6444页

作者： A.Arokiaraj Jovith S.Rama Sree Gudikandhula Narasimha Rao K.Vijaya Kumar Woong Cho Gyanendra Prasad Joshi Sung Won Kim Department of Networking and Communications College of Engineering and TechnologySRM Institute of Science and TechnologyKattankulathur603203India Department of Computer Science Engineering Aditya Engineering CollegeSurampalemAndhra Pradesh533437India Department of Computer Science and Engineering Vignan’s Institute of Information TechnologyVisakhapatnam530049India Department of Computer Science and Engineering Vignan’s Institute of Engineering for WomenVisakhapatnam530049India Department of Software Convergence Daegu Catholic UniversityGyeongsan38430Korea Department of Computer Science and Engineering Sejong UniversitySeoul05006Korea Department of Information and Communication Engineering Yeungnam UniversityGyeongsan-siGyeongbuk-do38541Korea

The exponential growth of data necessitates an effective data storage scheme,which helps to effectively manage the large quantity of *** accomplish this,Deoxyribonucleic Acid(DNA)digital data storage process can be employed,which encodes and decodes binary data to and from synthesized strands of *** quantization(VQ)is a commonly employed scheme for image compression and the optimal codebook generation is an effective process to reach maximum compression *** article introduces a newDNAComputingwithWater StriderAlgorithm based Vector Quantization(DNAC-WSAVQ)technique for Data Storage *** proposed DNAC-WSAVQ technique enables encoding data using DNA computing and then compresses it for effective data ***,the DNAC-WSAVQ model initially performsDNA encoding on the input images to generate a binary encoded *** addition,aWater Strider algorithm with Linde-Buzo-Gray(WSA-LBG)model is applied for the compression process and thereby storage area can be considerably *** order to generate optimal codebook for LBG,the WSA is applied to *** performance validation of the DNAC-WSAVQ model is carried out and the results are inspected under several *** comparative study highlighted the improved outcomes of the DNAC-WSAVQ model over the existing methods.

关键词： DNA computing data storage image compression vector quantization ws algorithm space saving

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Survey of methods and principles in three-dimensional reconstruction from two-dimensional medical images

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Visual Computing for Industry,Biomedicine,and Art 2023年第1期6卷 199-217页

作者： Mriganka Sarmah Arambam Neelima Heisnam Rohen Singh Department of Computer Science and Engineering National Institute of TechnologyNagaland 797103India Department of Information Technology Nagaland UniversityNagaland 797112India

Three-dimensional(3D)reconstruction of human organs has gained attention in recent years due to advances in the Internet and graphics processing *** the coming years,most patient care will shift toward this new ***,development of fast and accurate 3D models from medical images or a set of medical scans remains a daunting task due to the number of pre-processing steps involved,most of which are dependent on human *** this review,a survey of pre-processing steps was conducted,and reconstruction techniques for several organs in medical diagnosis were *** methods and principles related to 3D reconstruction were *** usefulness of 3D reconstruction of organs in medical diagnosis was also highlighted.

关键词： Three-dimensional reconstruction Human organ Medical images

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Review of Steganography and Cryptography: Tools for Covert Communications

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International Journal of Network Security 2025年第2期27卷 431-438页

作者： Fatima, Kanza Wu, Nan-I Chan, Chi-Shiang Hwang, Min-Shiang Department of Computer Science and Information Engineering Asia University Taichung41354 Taiwan Department of Information Management Lunghwa University of Science and Technology Taiwan Department of Artificial intelligence Asia University Taiwan

In today’s rapidly evolving digital media landscape, safeguarding content privacy and preventing unauthorized access to copyrighted material are major challenges. Cryptography plays a crucial role in modern digital media security, providing the essential tools to protect the confidentiality, reliability, and genuineness of digital media information. This paper examines cryptographic and steganographic techniques employed for securing digital media content. It provides an overview of key cryptographic principles, including symmetric and asymmetric encryption. It also provides different branches of steganography. This paper also discusses different evaluation matrices for cover object quality. Overall, this research offers a comprehensive exploration of steganography, highlighting its importance as a flexible communication method in the digital age and its future scope. © (2025), (International Journal of Network Security). All rights reserved.

关键词： information hiding

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TGFN-SD:A text-guided multimodal fusion network for swine disease diagnosis

Artificial Intelligence in Agriculture

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Artificial Intelligence in Agriculture 2025年第2期15卷 266-279页

作者： Gan Yang Qifeng Li Chunjiang Zhao Chaoyuan Wang Hua Yan Rui Meng Yu Liu Ligen Yu Research Center of Information Technology Beijing Academy of Agriculture and Forestry SciencesBeijing 100097China College of Computer and Information Engineering Tianjin Agricultural UniversityTianjin 300384China National Engineering Research Center for Information Technology in Agriculture Beijing 100097China Department of Agricultural Structure and Bioenvironmental Engineering College of Water Resources and Civil EngineeringChina Agricultural UniversityBeijing 100083China

China is the world's largest producer of pigs,but traditional manual prevention,treatment,and diagnosis methods cannot satisfy the demands of the current intensive production *** computer-aided diagnosis(CAD)systems for pigs are dominated by expert systems,which cannot be widely applied because the collection and maintenance of knowledge is difficult,and most of them ignore the effect of multimodal information.A swine disease diagnosis model was proposed in this study,the Text-Guided Fusion Network-Swine Diagnosis(TGFN-SD)model,which integrated text case reports and disease *** model integrated the differences and complementary information in the multimodal representation of diseases through the text-guided transformer module such that text case reports could carry the semantic information of disease images for disease ***,it alleviated the phenotypic overlap problem caused by similar diseases in combination with supervised learning and self-supervised *** results revealed that TGFN-SD achieved satisfactory performance on a constructed swine disease image and text dataset(SDT6K)that covered six disease classification datasets with accuracy and F1-score of 94.48%and 94.4%*** accuracies and F1-scores increased by 8.35%and 7.24%compared with those under the unimodal situation and by 2.02%and 1.63%compared with those of the optimal baseline model under the multimodal ***,interpretability analysis revealed that the model focus area was consistent with the habits and rules of the veterinary clinical diagnosis of pigs,indicating the effectiveness of the proposed model and providing new ideas and perspectives for the study of swine disease CAD.

关键词： computer-aided diagnosis Electronic health records Multimodal fusion Self-supervised learning Swine disease

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How far are we to GPT-4V?Closing the gap to commercial multimodal models with open-source suites

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Science China(information Sciences) 2024年第12期67卷 5-22页

作者： Zhe CHEN Weiyun WANG Hao TIAN Shenglong YE Zhangwei GAO Erfei CUI Wenwen TONG Kongzhi HU Jiapeng LUO Zheng MA Ji MA Jiaqi WANG Xiaoyi DONG Hang YAN Hewei GUO Conghui HE Botian SHI Zhenjiang JIN Chao XU Bin WANG Xingjian WEI Wei LI Wenjian ZHANG Bo ZHANG Pinlong CAI Licheng WEN Xiangchao YAN Min DOU Lewei LU Xizhou ZHU Tong LU Dahua LIN Yu QIAO Jifeng DAI Wenhai WANG State Key Laboratory for Novel Software Technology Nanjing University Shanghai AI Laboratory School of Computer Science Fudan University SenseTime Research Department of Information Engineering The Chinese University of Hong Kong Department of Electronic Engineering Tsinghua University

In this paper, we introduce InternVL 1.5, an open-source multimodal large language model(MLLM) to bridge the capability gap between open-source and proprietary commercial models in multimodal understanding. We introduce three simple improvements.(1) Strong vision encoder: we explored a continuous learning strategy for the large-scale vision foundation model — InternViT-6B, boosting its visual understanding capabilities, and making it can be transferred and reused in different LLMs.(2) Dynamic high-resolution: we divide images into tiles ranging from 1 to 40 of 448×448 pixels according to the aspect ratio and resolution of the input images, which supports up to 4K resolution input.(3) High-quality bilingual dataset: we carefully collected a high-quality bilingual dataset that covers common scenes, document images,and annotated them with English and Chinese question-answer pairs, significantly enhancing performance in optical character recognition(OCR) and Chinese-related tasks. We evaluate InternVL 1.5 through a series of benchmarks and comparative studies. Compared to both open-source and proprietary commercial models, InternVL 1.5 shows competitive performance, achieving state-of-the-art results in 8 of 18 multimodal benchmarks. Code and models are available at https://***/OpenGVLab/InternVL.

关键词： multimodal model open-source vision encoder dynamic resolution bilingual dataset

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Constructing a hybrid activation and parameter-fusion based CNN medical image classifier

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International Journal of information technology (Singapore) 2024年第5期16卷 3265-3272页

作者： Maree, Mohammed Zanoon, Tareq Dababat, Alya’ Awwad, Mays Department of Information Technology Faculty of Information Technology Arab American University 13 Zababdeh Jenin Palestine Department of Computer System Engineering Faculty of Engineering Arab American University Jenin Palestine

In recent years, numerous deep learning models for medical image classification have emerged, with varying accuracies influenced by factors like image quality, content, and the convoluted low-level features. In this article, we aim to identify key features that enhance the precision of convolutional neural networks (CNNs), which are widely employed in this domain. We empirically demonstrate that parameters like activation functions, image augmentation, convolutional layers, filter sizes, and max pooling significantly impact precision, while others like hidden layers, epochs, and batch sizes affect quality and runtime negatively. To validate our proposal, we use a publicly-available dataset of colored Breast Cancer images which comprises 27,751 images distributed over three sub-datasets as follows: 4162 testing images (2980 no cancer and 1182 with cancer), 19,426 training images (13,911 no cancer and 5515 with cancer), and 4163 validation images (2981 no cancer and 1182 with cancer). Multiple experimental runs were conducted, and the outcomes of each run were recorded, taking into account various evaluation metrics and components of the CNN architecture. Results demonstrate that the highest CNN classifier’s accuracy was achieved when coupling RelU and Sigmoid activation functions using a 3 × 3 filter with a batch of size 32 and 20 epochs and 2 hidden layers. A Similar accuracy rate (83.55%) has also been achieved when coupling ReLU and Sigmoid activation functions using the DensNet-201 pre-trained CNN model. © The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： Breast cancer CNN Image classification Multi-level architecture Neural networks Parameter fusion

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FoodExpert: Portable Intelligent Device for Rapid Screening of Pulse Quality and Adulteration

IEEE Transactions on AgriFood Electronics

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IEEE Transactions on AgriFood Electronics 2024年第1期2卷 2-11页

作者： Pandey, Harsh Arya, Subhanshu Das, Debanjan Udutalapally, Venkanna International Institute of Information Technology Naya Department of Electronics & Communication Engineering Naya Raipur493661 India National Institute of Technology Department of Computer Science & Engineering Warangal506004 India

Pulses are one of the most important food crops in the world due to their higher protein content, approximately 21%-25%. Therefore, it is crucial to analyze the crop's quality and impurity levels. Stones, pebbles, marble chips, and synthetic dyes, such as lead chromate, metanil yellow, and artificial colors, are some of the impurities added to pulse products, accidentally or on purpose. The existing analysis techniques are mostly laboratory-based, time-consuming, costly, and require human examination. To address this issue, this article presents an intelligent system, FoodExpert, based on image processing that automatically uses an image of a pulse sample to identify the region of interest and essential attributes. Then, machine learning frameworks are used to predict pulse quality and adulteration levels based on the obtained parameters. On the test dataset, the suggested model had a 96% accuracy rate for pulse quality prediction and 94% accuracy for adulteration level prediction. The model was successfully deployed on a Raspberry Pi-based hardware prototype and mobile application. © 2023 IEEE.

关键词： Feature extraction

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Exploiting Human Pose and Scene information for Interaction Detection

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computers, Materials & Continua 2023年第3期74卷 5853-5870页

作者： Manahil Waheed Samia Allaoua Chelloug Mohammad Shorfuzzaman Abdulmajeed Alsufyani Ahmad Jalal Khaled Alnowaiser Jeongmin Park Department of Computer Science Air UniversityIslamabad44000Pakistan Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Science College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia Department of Computer Engineering College of Computer Engineering and SciencesPrince Sattam Bin Abdulaziz UniversityAl-Kharj11942Saudi Arabia Department of Computer Engineering Korea Polytechnic UniversitySiheung-siGyeonggi-do237Korea

Identifying human actions and interactions finds its use in manyareas, such as security, surveillance, assisted living, patient monitoring, rehabilitation,sports, and e-learning. This wide range of applications has attractedmany researchers to this field. Inspired by the existing recognition systems,this paper proposes a new and efficient human-object interaction recognition(HOIR) model which is based on modeling human pose and scene featureinformation. There are different aspects involved in an interaction, includingthe humans, the objects, the various body parts of the human, and the backgroundscene. Themain objectives of this research include critically examiningthe importance of all these elements in determining the interaction, estimatinghuman pose through image foresting transform (IFT), and detecting the performedinteractions based on an optimizedmulti-feature vector. The proposedmethodology has six main phases. The first phase involves preprocessing theimages. During preprocessing stages, the videos are converted into imageframes. Then their contrast is adjusted, and noise is removed. In the secondphase, the human-object pair is detected and extracted from each image *** third phase involves the identification of key body parts of the detectedhumans using IFT. The fourth phase relates to three different kinds of featureextraction techniques. Then these features are combined and optimized duringthe fifth phase. The optimized vector is used to classify the interactions in thelast phase. TheMSRDaily Activity 3D dataset has been used to test this modeland to prove its efficiency. The proposed system obtains an average accuracyof 91.7% on this dataset.

关键词： Artificial intelligence daily activities human interactions human pose information image foresting transform scene feature information

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