检索结果-内蒙古大学图书馆

IEEE Transactions on Artificial Intelligence 2024年第9期5卷 4354-4363页

作者： Roy, Ayush Mohiuddin, S.K. Sarkar, Ram Jadavpur University Department of Electrical Engineering Kolkata700032 India Asutosh College Department of Computer Science Kolkata700026 India Jadavpur University Department of Computer Science and Engineering Kolkata700032 India

The process of modifying digital images has been made significantly easier by the availability of several image editing software. However, in a variety of contexts, including journalism, judicial processes, and historical documentation, the authenticity of images is of utmost importance. In particular, copy-move forgery is a distinct type of image manipulation, where a portion of an image is copied and pasted into another area of the same image, creating a fictitious or altered version of the original. In this research, we present a lightweight MultiResUnet architecture with the similarity-based positional attention module (SPAM) attention module for copy-move forgery detection (CMFD). By using a similarity measure across the patches of the features, this attention module identifies the patches, where a forged region is present. The lightweight network also aids in resource-efficient training and transforms the model into one that can be used in real time. We have employed four commonly used but extremely difficult CMFD datasets, namely CoMoFoD, COVERAGE, CASIA v2, and MICC-F600, to assess the effectiveness of our model. The proposed model significantly lowers false positives, thereby improving the pixel-level accuracy and dependability of CMFD tools. © 2020 IEEE.

关键词： Real time systems

来源：评论

学校读者我要写书评

暂无评论

Fuzzy conceptualization of the search queries

引用

International Journal of Information Technology (Singapore) 2024年第2期16卷 957-965页

作者： Sijin, P. Champa, H.N. Department of Computer Science and Engineering University Visvesvaraya College of Engineering Bangalore India

Deciding the context of the search query based on its representation over a concept network using fuzzy methods provides a better thrust to the overall search process. The existing context based search diversification methods emphasize the importance of the numerical representation of the query over a data repository and derive a query peace which is conceptually measured. The search operation can use these semantically meaningful segments as a confident segment in the conceptual network. The proposed Fuzzy Conceptual Search Model (FCM) derives a query string which is contextually measured by means of valid segmentation, Pairwise Type Detection and concept labeling on its Concept Network. The approach measures the semantic coherence among segments in the search query and prepares a valid query piece. The Maximal Clique Cycle Count (MCCC) algorithm is used in valid segmentation process. The novel randomized MCCC algorithm improved the quality of query segmentation process and considered shared segments and conceptual ring count to improvise the efficiency of the search. The segmented query paths are processed with proper instances to pinpoint the context of the search at an earlier phase. The instance ambiguity is removed by conducting a threshold pair wise modeling of typed terms followed by a weighted voting approach called Concept labeling. The proposed FCM shows good results in terms of precision for both query and tweet data sets with varying impact lexical feature indication values. © The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of computer Applications and Management 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s);author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

关键词： Concept labeling Concept network Conceptual ring Maximal clique Singleton score

来源：评论

学校读者我要写书评

暂无评论

Adaptive Cyber Defense Technique Based on Multiagent Reinforcement Learning Strategies

引用

Intelligent Automation & Soft Computing 2023年第6期36卷 2757-2771页

作者： Adel Alshamrani Abdullah Alshahrani Department of Cybersecurity College of Computer Science and EngineeringUniversity of JeddahJeddahSaudi Arabia Department of Computer Science and Artificial Intelligence College of Computer Science and EngineeringUniversity of JeddahJeddahSaudi Arabia

The static nature of cyber defense systems gives attackers a sufficient amount of time to explore and further exploit the vulnerabilities of information technology *** this paper,we investigate a problem where multiagent sys-tems sensing and acting in an environment contribute to adaptive cyber *** present a learning strategy that enables multiple agents to learn optimal poli-cies using multiagent reinforcement learning(MARL).Our proposed approach is inspired by the multiarmed bandits(MAB)learning technique for multiple agents to cooperate in decision making or to work *** study a MAB approach in which defenders visit a system multiple times in an alternating fash-ion to maximize their rewards and protect their *** find that this game can be modeled from an individual player’s perspective as a restless MAB *** discover further results when the MAB takes the form of a pure birth process,such as a myopic optimal policy,as well as providing environments that offer the necessary incentives required for cooperation in multiplayer projects.

关键词： Multiarmed bandits reinforcement learning multiagents intrusion detection systems

来源：评论

学校读者我要写书评

暂无评论

Detection of brain tumor using Hybridized 3D U-Net model on MRI images

引用

Multimedia Tools and Applications 2025年 1-29页

作者： Shreeharsha, J. Department of Computer Science and Engineering Rao Bahadur Y. Mahabaleswarappa Engineering College Karnataka Ballari India

The automatic detection of brain tumor is an emerging challenge because tumors vary in mass, nature, position, and similarities between the normal and brain lesions. First, the Magnetic Resonance Imaging (MRI) images are acquired from Brain Tumor Segmentation (BraTS) 2018, 2019 and 2020 databases;here, BraTS2018, BraTS2019 and BraTS2020 databases are considered as DB1, DB2 and DB3. Next, the image denoising is carried out by employing a normalization technique to enhance contrast and intensity levels of the acquired MRI images. Then, a hybridized 3D U-Net model is introduced for Brain Tumor Detection (BTD). In the hybridized 3D U-Net model, a ResNet-50 model is incorporated as a feature extractor in the conventional 3D U-Net model. In this particular context, the ResNet-50 model extracts intricate textures and patterns from the denoised 3D brain images, and assists the 3D U-Net model in accurate segmentation and classification of four modalities of brain tumors: T1, T2, Contrast-Enhanced (CE) -T1, and flair. In the segmentation phase, the hybridized 3D U-Net model obtains maximum segmentation results with a dice coefficient of 96.82%, 98.06% and 98.80%, and Jaccard coefficient of 96.64%, 97.25%, and 98.60% on the DB1, DB2, and DB2 databases. Additionally, in the classification phase, the hybridized 3D U-Net model achieves significant classification results with accuracies of 99.32%, 99.40%, and 99.80% on the DB1, DB2, and DB3 databases, correspondingly. The implementation of this research has been uploaded to the GitHub repository at https://***/shreeharsha-revanth/Hybrid-3D-UNet/. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Image segmentation

来源：评论

学校读者我要写书评

暂无评论

An Attention-Based CNN Framework for Alzheimer’s Disease Staging with Multi-Technique XAI Visualization

引用

computers, Materials & Continua 2025年第5期83卷 2947-2969页

作者： Mustafa Lateef Fadhil Jumaili Emrullah Sonuç Department of Computer Engineering Karabuk UniversityKarabük78050Türkiye Department of Computer Science College of Computer Science and MathematicsTikrit UniversityTikrit34001Iraq

Alzheimer’s disease(AD)is a significant challenge in modern healthcare,with early detection and accurate staging remaining critical priorities for effective *** Deep Learning(DL)approaches have shown promise in AD diagnosis,existing methods often struggle with the issues of precision,interpretability,and class *** study presents a novel framework that integrates DL with several eXplainable Artificial Intelligence(XAI)techniques,in particular attention mechanisms,Gradient-Weighted Class Activation Mapping(Grad-CAM),and Local Interpretable Model-Agnostic Explanations(LIME),to improve bothmodel interpretability and feature *** study evaluates four different DL architectures(ResMLP,VGG16,Xception,and Convolutional Neural Network(CNN)with attention mechanism)on a balanced dataset of 3714 MRI brain scans from patients aged 70 and *** proposed CNN with attention model achieved superior performance,demonstrating 99.18%accuracy on the primary dataset and 96.64% accuracy on the ADNI dataset,significantly advancing the state-of-the-art in AD *** ability of the framework to provide comprehensive,interpretable results through multiple visualization techniques while maintaining high classification accuracy represents a significant advancement in the computational diagnosis of AD,potentially enabling more accurate and earlier intervention in clinical settings.

关键词： Alzheimer’s disease deep learning early disease detection XAI medical image classification

来源：评论

学校读者我要写书评

暂无评论

RSSI-Based 3D Wireless Sensor Node Localization Using Hybrid T Cell Immune and Lotus Optimization

引用

computers, Materials & Continua 2024年第12期81卷 4833-4851页

作者： Weiwei Hu Kiran Sree Pokkuluri Rajesh Arunachalam Bander A.Jabr Yasser A.Ali Preethi Palanisamy School of Information Engineering Technology&MediaUniversity of HenanKaifeng475004China Department of Computer Science and Engineering Shri Vishnu Engineering College forWomenBhimavaram534202Andhra PradheshIndia Department of Electronics and Communication Engineering Saveetha School of EngineeringSaveetha Institute of Medical and Technical SciencesThandalam602105ChennaiIndia Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityRiyadh11543Saudi Arabia Department of Computer Science and Engineering Saveetha School of EngineeringSaveetha Institute of Medical and Technical SciencesThandalam602105ChennaiIndia

Wireless Sensor Network(WSNs)consists of a group of nodes that analyze the information from surrounding *** sensor nodes are responsible for accumulating and exchanging ***,node local-ization is the process of identifying the target node’s *** this research work,a Received Signal Strength Indicator(RSSI)-based optimal node localization approach is proposed to solve the complexities in the conventional node localization ***,the RSSI value is identified using the Deep Neural Network(DNN).The RSSI is conceded as the range-based method and it does not require special hardware for the node localization process,also it consumes a very minimal amount of cost for localizing the nodes in 3D *** position of the anchor nodes is fixed for detecting the location of the ***,the optimal position of the target node is identified using Hybrid T cell Immune with Lotus Effect Optimization algorithm(HTCI-LEO).During the node localization process,the average localization error is minimized,which is the objective of the optimal node *** the regular and irregular surfaces,this hybrid algorithm effectively performs the localization *** suggested hybrid algorithm converges very fast in the three-dimensional(3D)*** accuracy of the proposed node localization process is 94.25%.

关键词： Sensor node localization received signal strength indicator 3D wireless sensor network deep neural network average localization error and hybrid T cell immune with lotus effect optimization algorithm

来源：评论

学校读者我要写书评

暂无评论

RagaMoodSync: a deep learning framework for Hindustani Raga recommendations driven by facial emotions

引用

International Journal of Information Technology (Singapore) 2025年 1-14页

作者： Pingle, Yogesh Prabhakar Ragha, Lakshmappa K. Department of Computer Engineering Terna Engineering College University of Mumbai Maharashtra Mumbai India Department of Computer Science & Engineering (Data Science) Vidyavardhini’s College of Engineering & Technology University of Mumbai Maharashtra Mumbai India

RagaMoodSync presents an innovative framework for personalized music therapy through Hindustani Raga recommendations based on facial emotion recognition. Utilizing a Fusion-DenseNet (FusDenseNet) architecture with advanced multi-pooling for emotion detection, the system achieves high precision in identifying user moods. Integrated with a Focus Deep Collaborative Filtering (FDCF) recommendation system, RagaMoodSync delivers tailored raga suggestions that address emotional well-being and stress management. Validated with the FER2013 dataset, the framework demonstrates superior performance over existing approaches. By merging AI-driven emotion recognition with the therapeutic potential of Indian classical music, this system pioneers a novel intersection of technology and mental health care, offering a promising pathway for non-invasive emotional healing solutions. © Bharati Vidyapeeth's Institute of computer Applications and Management 2025.

关键词： Focus Deep Collaborative Filtering FusDenseNet RagaMoodSync

来源：评论

学校读者我要写书评

暂无评论

An Upgraded Approach for Identifying Partially Reduplicated Forms in Bengali Text

引用

SN computer science 2024年第7期5卷 892页

作者： Barman, Abhijit Saha, Diganta Pal, Alok Ranjan Department of Computer Science and Engineering Jadavpur University Kolkata 700032 India Department of Computer Science and Engineering College of Engineering and Management Kolaghat 721171 India

This paper presents a concise methodology for the detection of partially reduplicated Multi-Word Expressions (MWEs) in Bengali texts. The entire process of identifying such reduplicated forms is carried out in two distinct phases, each contributing to the accuracy and effectiveness of the overall approach. In the first phase, a Levenshtein distance based algorithm is employed to identify partial reduplicated forms within Bengali text. This algorithm assesses the similarity between words and determines whether a partially reduplicated structure exists, thereby flagging relevant instances of reduplicated forms. Moving to the second phase, the performance of the first phase is enhanced through the application of a noble technique known as Word Expansion. By doing so, the performance of the reduplication identification process are significantly improved, leading to more accurate results. Evaluation metrics include Precision (90.00%), Recall (85.71%), and F1-Score (87.80%). These high scores underscore the system’s capability to successfully identify and categorize partially reduplicated MWEs within Bengali text. Moreover, the performance metrics also demonstrate that this approach surpasses the current state-of-the-art methods for identifying reduplicated expressions in Bengali text, reaffirming its efficacy and relevance. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Bi-gram Levenshtein distance Multi-word expression (MWE) Natural language processing (NLP) Reduplication Tokenization Word expansion

来源：评论

学校读者我要写书评

暂无评论

Optimal feature with modified bi-directional long short-term memory for big data classification in healthcare application

引用

International Journal of Information Technology (Singapore) 2024年第7期16卷 4441-4450页

作者： Kamble, Subhash Arunalatha, J.S. Venugopal, K.R. Computer Science and Engineering Bangalore University Department of Computer Science and Engineering University Visvesvaraya College of Engineering Bangalore University Bangalore University

Artificial intelligence together with its applications are advancing in all fields, particularly medical science. A considerable quantity of clinical data is available, yet the vast majority of it is wasted. It will be valuable in diagnosing human life early if it is applied effectively. An excellent classification system assists medical practitioners in providing a suitable diagnosis at a younger stage. Medical data typically contain a large number of features, and including all of them in decision making may result in over fitting of the classification model, lowering accuracy. As a result, an effective dimensionality reduction strategy that minimizes the amount of features while also improving classification accuracy is required. Therefore in this study, efficient dimension reduction-based medical data classification is proposed. The proposed approach is made up of three stages namely, pre-processing, feature selection, and classification. Initially, the medical data are collected and pre-processed. Then, to minimize computational complexity and time consumption, optimal features are selected using the adaptive rat swarm optimization (ARWO) algorithm. Then, the selected features are given to the modified bi-directional long short-term memory (MBi-LSTM) classifier to classify data as normal or abnormal. Then, the performance of the model is evaluated using different datasets and the effectiveness of the proposed model is analyzed in terms of different metrics. The proposed model implemented in python. © Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： ARWO Big data MBi-LSTM Medical data Optimal features

来源：评论

学校读者我要写书评

暂无评论

A Novel Approach to Energy Optimization:Efficient Path Selection in Wireless Sensor Networks with Hybrid ANN

引用

computers, Materials & Continua 2024年第5期79卷 2945-2970页

作者： Muhammad Salman Qamar Ihsan ulHaq Amil Daraz Atif MAlamri Salman A.AlQahtani Muhammad Fahad Munir Department of Electrical and Computer Engineering International Islamic UniversityIslamabad44000Pakistan School of Information Science and Engineering NingboTech UniversityNingbo315100China Software Engineering Department College of Computer and Information SciencesKing Saud UniversityRiyadh11495Saudi Arabia Computer Engineering Department College of Computer and Information SciencesKing Saud UniversityRiyadh11495Saudi Arabia

In pursuit of enhancing the Wireless Sensor Networks(WSNs)energy efficiency and operational lifespan,this paper delves into the domain of energy-efficient routing ***,the limited energy resources of Sensor Nodes(SNs)are a big challenge for ensuring their efficient and reliable *** data gathering involves the utilization of a mobile sink(MS)to mitigate the energy consumption problem through periodic network *** mobile sink(MS)strategy minimizes energy consumption and latency by visiting the fewest nodes or predetermined locations called rendezvous points(RPs)instead of all cluster heads(CHs).CHs subsequently transmit packets to neighboring *** unique determination of this study is the shortest path to reach *** the mobile sink(MS)concept has emerged as a promising solution to the energy consumption problem in WSNs,caused by multi-hop data collection with static *** this study,we proposed two novel hybrid algorithms,namely“ Reduced k-means based on Artificial Neural Network”(RkM-ANN)and“Delay Bound Reduced kmeans with ANN”(DBRkM-ANN)for designing a fast,efficient,and most proficient MS path depending upon rendezvous points(RPs).The first algorithm optimizes the MS’s latency,while the second considers the designing of delay-bound paths,also defined as the number of paths with delay over bound for the *** methods use a weight function and k-means clustering to choose RPs in a way that maximizes efficiency and guarantees network-wide *** addition,a method of using MS scheduling for efficient data collection is *** simulations and comparisons to several existing algorithms have shown the effectiveness of the suggested methodologies over a wide range of performance indicators.

关键词： Wireless Sensor Networks(WSNs) mobile sink(MS) rendezvous point(RP) machine learning Artificial Neural Networks(ANNs)

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：