检索结果-内蒙古大学图书馆

Hybrid image encryption using advanced least significant bit algorithm, chaotic maps and DNA encoding for digital healthcare

引用

Journal of Intelligent and Fuzzy Systems 2024年第4期46卷 9139-9153页

作者： Selvakumar, B. Abinaya, P. Lakshmanan, B. Sheron, S. Smitha Rajini, T. Department of Computer Science and Engineering Mepco Schlenk Engineering College Sivakasi India

Security and privacy are major concerns in this modern world. Medical documentation of patient data needs to be transmitted between hospitals for medical experts opinions on critical cases which may cause threats to the data. Nowadays most of the hospitals use electronic methods to store and transmit data with basic security measures, but these methods are still vulnerable. There is no perfect solution that solves the security problems in any industry, especially healthcare. So, to cope with the arising need to increase the security of the data from being manipulated the proposed method uses a hybrid image encryption technique to hide the data in an image so it becomes difficult to sense the presence of data in the image while transmission. It combines Least Significant Bit (LSB) Algorithm using Arithmetic Division Operation along with Canny edge detection to embed the patient data in medical images. The image is subsequently encrypted using keys of six different chaotic maps sequentially to increase the integrity and robustness of the system. Finally, an encrypted image is converted into DNA sequence using DNA encoding rule to improve reliability. The experimentation is done on the Chest XRay image, Knee Magnetic Resonance Imaging (MRI) image, Neck MRI image, Lungs Computed Tomography (CT) Scan image datasets and patient medical data with 500 characters, 1000 characters and 1500 characters. And, it is evaluated based on time coefficient of encryption and decryption, histogram, entropy, similarity score (Mean Square Error), quality score (peak signal-to-noise ratio), motion activity index (number of changing pixel rate), unified average changing intensity, image similarity score (structure similarity index measurement) between original and encrypted images. Also, the proposed technique is compared with other recent state of arts methods for 500 characters embedding and performed better than those techniques. The proposed method is more stable and embeds comparativel

关键词： Mean square error

来源：评论

学校读者我要写书评

暂无评论

Network life-time maximisation with low-power consumption by the usage of ANFIS-based technique in wireless sensor networks

引用

International Journal of Wireless and Mobile Computing 2024年第1期26卷 1-8页

作者： Rao, N. Srinivas Rama Rao, K.V.S.N. Department of Computer Science and Engineering KLEF Deemed to be University Andhra Pradesh Guntur India Department of Computer Science and Engineering KLEF Deemed to be University Telangana Hyderabad India

Clustering strategies for reducing the energy consumption and extending the network life have been employed widely in Wireless Sensor Network (WSN). The clustering mechanism can extend the network’s service life and network failure in WSN. In the study, we proposed the technique for improving network performance with a new energy efficient Adaptive Neuro-Fuzzy Inference System (ANFIS)-based routing approach for WSN. A new distributed cluster creation methodology that enables the self-organisation of local nodes, a novel method for the adjustment of clusters and the turning of the Cluster Head (CH) centre location to distribute energy burden equally through all sensing nodes incorporates the suggested ANFIS-based routing. The simulation result shows that the proposed scheme outperforms conventional methods with an improvement of 80% in network lifetime. Copyright © 2024 Inderscience Enterprises Ltd.

关键词： Base stations

来源：评论

学校读者我要写书评

暂无评论

A systematic literature review of video forgery detection techniques

引用

Multimedia Tools and applications 2025年 1-51页

作者： Aulakh, Manpreet Kaur Kanwal, Navdeep Bansal, Manish Department of Computer Science and Engineering Punjabi University Patiala Punjab Patiala India Department of Computer Science Baba Farid Group of Institutions Punjab Bathinda India

In the last decade, due to the widespread and inexpensive availability of digital video cameras, digital videos (DV) are employed for security purposes daily, and they are generally regarded as a more credible form of evidence than still photographs. Due to the tremendous growth of video editing tools, anyone with access to advanced editing software and a modern Smartphone can easily do digital video manipulations and fake it. As a result, to utilize video content as proof in court, it is necessary to evaluate and determine whether it is original or modified. To check the integrity and validity of video recordings, digital forgery detection techniques are required. The objective of the study is to present a systematic review of techniques for detecting forgery in digital videos. We conducted a systematic literature review (SLR) in this study to present a detailed review of the initial and recent research efforts in Digital video forgery detection, summarizing 260 relevant papers from 2000 to 2023 that have presented a variety of techniques. For analysis, we have presented our references in three different ways: according to the type of forgery detected, according to the type of model or technique used and according to the feature used for forgery detection. We look through the several datasets that are cited in articles and determine their applicable domain. Then, we looked at the numerous measuring metrics employed by different research papers and compared the effectiveness of deep and non-deep models in each category of forgery that was found. Finally, research gaps concerning passive video forgery detection are classified and highlighted. A comparison between our survey and other existing survey articles has been presented in the paper. Researchers who wish to work on video forgery detection will get assistance to determine what kind of efforts in forgery detection work is still required. This survey will also help to select techniques and features based on their

关键词： Video recording

来源：评论

学校读者我要写书评

暂无评论

Ensemble Machine Learning-Based Approach to Predict Cervical Cancer with Hyperparameter Tuning and Model Explainability

引用

Biomedical Materials and Devices 2025年 1-28页

作者： Uddin, Khandaker Mohammad Mohi Bhuiyan, Md. Tofael Ahmed Saad, Mirza Nadim Islam, Ashfaqul Islam, Md. Manowarul Department of Computer Science and Engineering Southeast University Dhaka 1208 China Department of Computer Science and Engineering University of Dhaka Dhaka Bangladesh Department of Computer Science and Engineering Jagannath University Dhaka India

Cervical cancer remains the top killer of women at a young age in the world, 85% of cases are detected in low-income countries. Preventive measures and therapeutic response are enhanced if potential hazards are identified early. This research belongs to this field by introducing an end-to-end prediction model based on individual medical records and early screening data thus emphasizing the discovery of meaningful predictors. In order to overcome issues with feature selection and class imbalances, our study creates an ensemble prediction framework that blends Random Forest and Logistic Regression techniques. In addition to achieving an astounding accuracy of 99.75%, the model guarantees transparency in its decision-making processes by utilizing sophisticated machine learning algorithms in conjunction with interpretability tools like SHAP and LIME, which is essential for applications in healthcare. The creation of an extensive ensemble method that combines several machine learning classifiers, advanced feature selection techniques for locating important predictive factors, and interpretability techniques to help healthcare professionals better understand complex predictions are some of the research’s main investments. By offering accurate and comprehensible risk assessments, this novel method has the potential to revolutionize clinical decision-making and enhance early cancer of the cervical cavity identification. This research promotes the development of more proactive and individualized cancer screening methods by fusing cutting-edge computational technology with medical diagnostics, improving health outcomes everywhere. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Cervical cancer Explainable machine learning Hybrid model introduction Hyperparameter tuning feature selection Machine learning Stacking ensemble technique

来源：评论

学校读者我要写书评

暂无评论

Detection of Acute Lymphoblastic Leukemia Using a Novel Bone Marrow Image Segmentation

引用

Tsinghua science and Technology 2025年第2期30卷 610-623页

作者： M.Anline Rejula Ben M Jebin Ravi Selvakumar S.Amutha Eberlein George Department of Computer Applications-PG Scott Christian College(Autonomous)Nagercoil 629003India Department of Computer Science Malankara Catholic CollegeMariagiri 629153India School of Computer Science and Engineering Vellore Institute of TechnologyChennai 600127India Department of Pediatrics Government Medical College and HospitalGovernment of Tamil NaduNamakkal 637001India

In our study,we present a novel method for automating the segmentation and classification of bone marrow images to distinguish between normal and Acute Lymphoblastic Leukaemia(ALL).Built upon existing segmentation techniques,our approach enhances the dual threshold segmentation process,optimizing the isolation of nucleus and cytoplasm *** is achieved by adapting threshold values based on image characteristics,resulting in superior segmentation outcomes compared to previous *** address challenges,such as noise and incomplete white blood cells,we employ mathematical morphology and median filtering *** methods effectively denoise the images and remove incomplete cells,leading to cleaner and more precise ***,we propose a unique feature extraction method using a hybrid discrete wavelet transform,capturing both spatial and frequency *** allows for the extraction of highly discriminative features from segmented images,enhancing the reliability of *** classification purposes,we utilize an improved Adaptive Neuro-Fuzzy Inference System(ANFIS)that leverages the extracted *** enhanced classification algorithm surpasses traditional methods,ensuring accurate identification of acute lymphoblastic *** innovation lies in the comprehensive integration of segmentation techniques,advanced denoising methods,novel feature extraction,and improved *** extensive evaluation on bone marrow samples from the Acute Lymphoblastic Leukemia Image DataBase(ALL-IDB)for Image Processing database using MATLAB 10.0,our method demonstrates outstanding classification *** segmentation accuracy for various cell types,including Band cells(96%),Metamyelocyte(99%),Myeloblast(96%),***(97%),***(97%),and Neutrophil cells(98%),further underscores the potential of our approach as a high-quality tool for ALL diagnosis.

关键词： Adaptive Neuro-Fuzzy Inference System(ANFIS) blood cells segmentation leukemia classification

来源：评论

学校读者我要写书评

暂无评论

Topological Evaluation of Certain computer Networks by Contraharmonic-Quadratic Indices

引用

computers, Materials & Continua 2023年第2期74卷 3795-3810页

作者： Ahmed M.Alghamdi Khalid Hamid Muhammad Waseem Iqbal M.Usman Ashraf Abdullah Alshahrani Adel Alshamrani Department of Software Engineering College of Computer Science and EngineeringUniversity of JeddahJeddah21493Saudi Arabia Department of Computer Science Superior UniversityLahore54000Pakistan Department of Software Engineering Superior UniversityLahore54000Pakistan Department of Computer Science GC Women UniversitySialkot53310Pakistan Department of Computer Science and Artificial Intelligence College of Computer Science and EngineeringUniversity of JeddahJeddah21493Saudi Arabia Department of Cybersecurity College of Computer Science and Engineering at University of JeddahJeddah21493Saudi Arabia

In various fields,different networks are used,most of the time not of a single kind;but rather a mix of at least two *** kinds of networks are called bridge networks which are utilized in interconnection networks of PC,portable networks,spine of internet,networks engaged with advanced mechanics,power generation interconnection,bio-informatics and substance intensify *** number that can be entirely calculated by a graph is called graph *** mathematical graph invariants have been portrayed and utilized for connection investigation during the latest twenty ***,no trustworthy evaluation has been embraced to pick,how much these invariants are associated with a network graph or subatomic *** this paper,it will discuss three unmistakable varieties of bridge networks with an incredible capacity of assumption in the field of computer science,chemistry,physics,drug industry,informatics and arithmetic in setting with physical and manufactured developments and networks,since Contraharmonic-quadratic invariants(CQIs)are recently presented and have different figure qualities for different varieties of bridge graphs or *** study settled the geography of bridge graphs/networks of three novel sorts with two kinds of CQI and Quadratic-Contraharmonic Indices(QCIs).The deduced results can be used for the modeling of the above-mentioned networks.

关键词： Bridge networks invariants Quadratic-Contraharmonic Indices maple network graph molecular graph

来源：评论

学校读者我要写书评

暂无评论

Enhancing cyber threat detection with an improved artificial neural network model

引用

Data science and Management 2025年第1期8卷 107-115页

作者： Oyinloye, Toluwase Sunday Arowolo, Micheal Olaolu Prasad, Rajesh Department of Computer Science African University of Science and Technology Abuja900107 Nigeria Department of Electrical Engineering and Computer Science University of Missouri ColumbiaMO65201 United States

Identifying cyberattacks that attempt to compromise digital systems is a critical function of intrusion detection systems (IDS). Data labeling difficulties, incorrect conclusions, and vulnerability to malicious data injections are only a few drawbacks of using machine learning algorithms for cybersecurity. To overcome these obstacles, researchers have created several network IDS models, such as the Hidden Naive Bayes Multiclass Classifier and supervised/unsupervised machine learning techniques. This study provides an updated learning strategy for artificial neural network (ANN) to address data categorization problems caused by unbalanced data. Compared to traditional approaches, the augmented ANN's 92% accuracy is a significant improvement owing to the network's increased resilience to disturbances and computational complexity, brought about by the addition of a random weight and standard scaler. Considering the ever-evolving nature of cybersecurity threats, this study introduces a revolutionary intrusion detection method. © 2024 Xi'an Jiaotong University

关键词： Network intrusion

来源：评论

学校读者我要写书评

暂无评论

Making Sense of Big Data in Intelligent Transportation Systems: Current Trends, Challenges and Future Directions

引用

ACM Computing Surveys 2025年第8期57卷 1-43页

作者： Ahmad Jan, Mian Adil, Muhammad Brik, Bouziane Harous, Saad Abbas, Sohail Department of Computer Science University of Sharjah Sharjah27272 United Arab Emirates Department of Computer Science and Engineering University at Buffalo BuffaloNY United States

Intelligent Transportation Systems (ITS) generate massive amounts of Big Data through both sensory and non-sensory platforms. The data support batch processing as well as stream processing, which are essential for reliable operations on the roads and connected vehicles in ITS. Despite the immense potential of Big Data intelligence in ITS, autonomous vehicles are largely confined to testing and trial phases. The research community is working tirelessly to improve the reliability of ITS by designing new protocols, standards, and connectivity paradigms. In the recent past, several surveys have been conducted that focus on Big Data Intelligence for ITS, yet none of them have comprehensively addressed the fundamental challenges hindering the widespread adoption of autonomous vehicles on the roads. Our survey aims to help readers better understand the technological advancements by delving deep into Big Data architecture, focusing on data acquisition, data storage, and data visualization. We reviewed sensory and non-sensory platforms for data acquisition, data storage repositories for archival and retrieval of large datasets, and data visualization for presenting the processed data in an interactive and comprehensible format. To this end, we discussed the current research progress by comprehensively covering the literature and highlighting challenges that urgently require the attention of the research community. Based on the concluding remarks, we argued that these challenges hinder the widespread presence of autonomous vehicles on the roads. Understanding these challenges is important for a more informed discussion on the future of self-driven technology. Moreover, we acknowledge that these challenges not only affect individual layers but also impact the functionality of subsequent layers. Finally, we outline our future work that explores how resolving these challenges could enable the realization of innovations such as smart charging systems on the roads and data centers

关键词： Intelligent transportation systems big data sensors storage visualization data centers on the wheels smart computers on the roads

来源：评论

学校读者我要写书评

暂无评论

Deep Learning Model-Based Region Measurement Analysis for Breast Cancer Classification 1

Deep Learning Model-Based Region Measurement Analysis for Br...

引用

1st International Conference for Women in Computing, InCoWoCo 2024

作者： Meegada, Radhika Bhuyan, Hemanta Kumar Department of Computer Science Engineering Andhra Pradesh Guntur India Department of Information Technology and Computer Applications Andhra Pradesh Guntur India

ISBN: (纸本)9798331518943

The mortality of Breast cancer patients is issued based on lack of identification and treatment, and mammography is a useful tool for early screening. Deep learning-based computer-aided diagnosis (CAD) of mammography can help radiologists make more accurate and objective decisions. However, a lot of the current approaches rely on datasets that have been manually annotated for segmentation. Furthermore, several techniques are less interested in using ROI for image fusion models because of the high image sizes and modest lesion proportions. These flaws raise the application of the model's labour, cost, and computational overhead. Consequently, a network with regional scoring based on the deep location network (DLN) model is suggested. Utilizing pooling modules, DLN is considered a single feature extractor end-to-end mammography image classification approach that locates lesion sites without the need for training. Different deep learning models have been considered to test our proposed model. The experiments are demonstrated on publicly available IN breast and CBIS-DDSM datasets, and the proposed model fared well when compared to earlier cutting-edge techniques for classifying mammogram images. © 2024 IEEE.

关键词： Mammography

来源：评论

学校读者我要写书评

暂无评论

Enhancing English accent identification in automatic speech recognition using spectral features and hybrid CNN-BiLSTM model

引用

Multimedia Tools and applications 2025年 1-28页

作者： Ahmed, Ghayas Lawaye, Aadil Ahmad Jain, Vishal Chatterjee, Jyotir Moy Mahajan, Shubham Department of Computer Sciences Baba Ghulam Shah Badshah University J&K Rajouri India Department of Computer Science & Engineering Sharda University Noida India Dehradun India Department of Computer Science & Engineering Amity University Haryana India

Automatic Speech Recognition (ASR) has been the regnant research area in the domain of Natural Language Processing for the last few decades. Past years’ advancement provides progress in this area of research. The accent of spoken is the prominent factor affecting the performance of ASR. Accent is the pronunciation of a word in a distinct way which may vary from specimen to specimen depending upon their social class, formality, age, vocal property, gender, geography, and influence of native or other language. Recognizing an accent requires various complex characteristics and features of voice such as voice quality, prosody, and phoneme pronunciation which are difficult to extract and analyze. To solve these difficulties, the researchers focus their attention on spectral representation such as Mel Spectrogram and Mel-Frequency Cepstral Coefficient (MFCC). In this study, we will analyze which features achieve maximum accuracy for the English accent classification task. Here in this work, we perform our experiments on Log Mel filter bank and MFCC features with four different window function. We extract features from raw audio with various windowing function and train a hybrid model of Convolutional Neural Network and Bidirectional Long Short-term Memory network (CNN-BiLSTM) on a custom dataset having nine different accents of English language namely American, Australian, British, Indian (Oriya, Bangla, Telegu, Malayalam), and Welsh. The accuracy of each feature is evaluated and compared. The log Mel filter bank outputs the highest accuracy of 99.75% and 99.91% of training and validation with Han window function while MFCC with Bartlett window function achieves 99.98% of training and 99.33% of validation accuracy. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Spectrographs

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：