检索结果-内蒙古大学图书馆

Salp swarm algorithm using lens opposition-based learning and local search for constrained optimization problems

Iran Journal of computer science 2025年第2期8卷 659-686页

作者： Majumdar, Parijata Mitra, Sanjoy Department of Computer Science and Engineering Indian Institute of Information Technology Agartala Jirania 799046 India Department of Computer Science and Engineering Tripura Institute of Technology Agartala Narsingarh 799009 India

The Salp swarm algorithm (SSA) simulates how salps forage and travel in the ocean. SSA suffers from low initial population diversity, improper balancing of exploration and exploitation, and slow convergence speed. Thus, we have proposed a novel variant of SSA (LOLSSA) by integrating lens opposition learning with the fundamental evolutionary techniques of SSA to increase the initial population variety. During exploration, the information about the local best position for each individual is effectively shared with the follower, using the salp position update process to avoid getting stuck at the local optimum. At the end of each iteration, the local search algorithm is utilized to increase exploitation. Additionally, the leader’s salp position incorporates inertia weight to speed up the convergence speed. Swarm intelligence algorithms have proven their efficacy in feature selection, since they search for an optimal set of features in a large search area that will have the best impact on the accuracy of the training system. LOLSSA is thus employed for feature selection to select informative features in 11 high-dimensional datasets, where it outperforms well-known metaheuristics in average fitness, accuracy, and average number of feature reductions. In the software-as-a-service cloud computing model, a task scheduling problem is solved to satisfy the QoS requirement of both the service provider and the user. Experiments show that LOLSSA optimizes this problem in terms of different performance metrics. LOLSSA also outperforms other metaheuristics regarding 23 well-known benchmark functions, IEEE CEC-C06 2019, and CEC-06 2020 benchmark functions, and in solving engineering design problems. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

关键词： Convergence speed Feature selection Global optimization Lens opposition learning Salp swarm algorithm Task scheduling problem

来源：评论

学校读者我要写书评

暂无评论

Exploring image data association: A hybrid mining approach

引用

Multimedia Tools and Applications 2025年第9期84卷 5725-5740页

作者： Parashar, Nishtha Tiwari, Akhilesh Gupta, Rajendra Kumar Department of Computer Science and Engineering Madhav Institute of Technology & Science M.P. Gwalior India Department of Information Technology Madhav Institute of Technology & Science M.P. Gwalior India

In this paper, a new approach for mining image association rules is presented, which involves the fine-tuned CNN model, as well as the proposed FIAR and OFIAR algorithms. Initially, the image transactional database is generated using feature vectors obtained from the fine-tuned CNN architecture. The proposed FIAR algorithm is used to generate hash-indexed image association rules, which are further optimized using the proposed OFIAR algorithm. This methodology combines the strengths of the CNN model to extract histogram features from images, the FIAR algorithm to efficiently mine frequent image itemsets, and the OFIAR algorithm to optimize image association rules. The proposed methodology can be used to discover hidden relationships among images, leading to new insights in image processing and analysis. Efficient results were obtained with a minimum support of 0.50 and a minimum confidence of 0.50. Experiments were performed on the fruits image dataset consisting of 2618 images from six different classes, and the results show that image mining is feasible and can produce strong optimized image association rules that can be further used for classification purposes. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Association rules

来源：评论

学校读者我要写书评

暂无评论

Stationary wavelet transform and SpinalNet trained light spectrum Tasmanian devil optimization enabled DR detection using fundus images

引用

Multimedia Tools and Applications 2025年第4期84卷 1597-1626页

作者： Tamboli, Mubin Shoukat Dhumane, Amol Prasad, Rajesh Prasad, Jayashree Rajesh Ranjan, Nihar M. Computer Engineering Pimpri Chinchwad College of Engineering Pradhikaran Maharashtra Pune411044 India Computer Science and Engineering Symbiosis Institute of Technology Maharashtra Pune India Computer Science and Engineering School of Computing MIT Art Design and Technology University Loni Kalbhor Maharashtra Pune412201 India Information Technology JSPM’s Rajarshi Shahu College of Engineering Tathawade Maharashtra Pune411033 India

Diabetes is a common disease that causes complications in the eyes known as Diabetic Retinopathy (DR). It aids in discovering the DR and tends to be the main cause behind people’s blindness amidst the previous decades. For prevent the complications of DR and to control the level of blood Glucose, the timely and crucial diagnosis is essential. A model is provided for detecting the DR. Here, Light Spectrum Tasmanian Devil Optimization (LSTDO) is utilized with SpinalNet for DR detection. The method undergoes an adaptive median filter and Region of interest mining process to generate a pre-processed image. The Sparse Fuzzy C-Means (FCM) is adapted for segmenting the optic disks followed by feature extraction. Then, the Hough line transformation with Tyler Coye algorithm (HLTCA) is used for blood vessel segmentation. Concurrently, the Stationary Wavelet Transform (SWT) is adapted to input image to overwhelm the deficiency of translation-invariance issue and then the feature mining is done. The feature vector is generated by unifying the outputs of the three-feature extraction phases and is employed to detect DR. The accuracy, sensitivity, and specificity obtained by the LSTDO-based SpinalNet are 91.6%, 89.9% and 92.1%, respectively. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Feature extraction

来源：评论

学校读者我要写书评

暂无评论

Enhancing Data Forwarding Efficiency in SIoT with Multidimensional Social Relations

引用

computers, Materials & Continua 2024年第1期78卷 1095-1113页

作者： Fang Xu Songhao Jiang Yi Ma Manzoor Ahmed Zenggang Xiong Yuanlin Lyu School of Computer and Information Science Hubei Engineering UniversityXiaogan432000China School of Computer Science and Information Engineering Hubei UniversityWuhan430062China Artificial Intelligence Industrial Technology Research Institute Hubei Engineering UniversityXiaogan432000China

Effective data communication is a crucial aspect of the Social Internet of Things(SIoT)and continues to be a significant research *** paper proposes a data forwarding algorithm based on Multidimensional Social Relations(MSRR)in SIoT to solve this *** proposed algorithm separates message forwarding into intra-and cross-community forwarding by analyzing interest traits and social connections among *** new metrics are defined:the intensity of node social relationships,node activity,and community *** the community,messages are sent by determining which node is most similar to the sender by weighing the strength of social connections and node *** a node performs cross-community forwarding,the message is forwarded to the most reasonable relay community by measuring the node activity and the connection between *** proposed algorithm was compared to three existing routing algorithms in simulation *** indicate that the proposed algorithmsubstantially improves message delivery efficiency while lessening network overhead and enhancing connectivity and coordination in the SIoT context.

关键词： SIoT data forwarding social attributes social relations community

来源：评论

学校读者我要写书评

暂无评论

An autoencoder based unsupervised clustering approach to analyze the effect of E-learning on the mental health of Indian students during the Covid-19 pandemic

引用

Multimedia Tools and Applications 2024年 1-21页

作者： Banerjee, Pritha Jana, Chandan Saha, Jayita Chowdhury, Chandreyee Computer Application and Science Institute of Engineering and Management West Bengal Kolkata700091 India Computer Science and Engineering Jadavpur University West Bengal Kolkata700032 India Computer Science and Engineering Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Manipal Karnataka Bengaluru560064 India

Due to the Covid-19 pandemic, the education system in India has changed to remote that is, online study mode. Though there are works on the effect of teaching learning on Indian students, the effect of online mode and associated mental state, particularly when the entire country is going through a crisis could not be found in the literature. Our goal is to analyze data and find some pattern through which we can understand the effectiveness of the online study and also try to figure out the stress level. The dataset we collected from 500 undergraduate college students during April-May, 2021 is in questionnaire format. Our contribution in this paper are - (i) publishing a dataset of student feedbacks, and (ii) designing a data processing pipeline involving autoencoders followed by clustering approach. The dataset is in text format so for our analysis we have converted the dataset into a numerical format using the concept of a binary bag of words. Dimensionality reduction is applied through autoencoder for an effective latent space representation. Finally, for finding patterns out of this dimensionally reduced feature space, we have applied unsupervised learning algorithms - kMeans and DBSCAN. A thorough analysis of the clustering process reveals that the absence of social communication in purely online education provokes isolation irrespective of the urban or rural background of the students. However, it could supplement offline classes as a substantial number of students welcomed the concept of online learning as reported in the data. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Students

来源：评论

学校读者我要写书评

暂无评论

Multilevel characterization of unknown protein sequences using hierarchical long short term memory model

引用

Multimedia Tools and Applications 2025年第9期84卷 6593-6614页

作者： Agrawal, Saurabh Sisodia, Dilip Singh Nagwani, Naresh Kumar Department of Computer Science & Engineering National Institute of Technology Raipur GE Road Chhattisgarh Raipur492010 India

Multilevel characterization of the recently developed Unknown Protein Sequence (UPS) is significant for the drug-designing, disease-diagnosis, and treatment plans. UPS can demonstrate harmful as well as useful characters. Long Short Term Memory (LSTM) based Multilevel Protein Characterization (ML_PC) model using Neuro-Fuzzy Classifier (NFC) and transfer learning has been proposed in this paper for characteristics, species, and function prediction of the UPS. Gram Negative (GN) dataset with known 2286 protein sequences, is used for formation and validation of the ML_PC model. Thereafter NFC is applied at initial stage of the validated ML_PC model to filter the 518 independent protein sequences of the UPS dataset as GN or Non-GN in context with known GN dataset. However, NFC is competent to handle likelihood classification complications of supervised learning. Characteristics, species, and functions of the filtered GN UPS have been predicted in succeeding levels of the ML_PC framework through Protein Characterization (PC) model. LSTM and transfer learning based PC model has been trained and validated using augmented feature of the known GN protein sequence samples, while tested with UPS and filtered GN protein sequences. Transfer learning has been applied as requisite postprocessing to overcome overfitting in the prediction of bacterial protein species with insufficient protein sequence samples. Performance of the multilevel model is improved by 7.52% after combining with transfer learning and NFC. ML_PC model has been compatible to extract relevant biological information of the UPS in context to various categories of bacterial proteins. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Long short-term memory

来源：评论

学校读者我要写书评

暂无评论

Unique hash value based identical video sharing traffic flow optimization across social networks

引用

Multimedia Tools and Applications 2025年第13期84卷 11983-12011页

作者： Konyak, Chenlep Yakha Baydeti, Nagaraju Department of Computer Science and Engineering National Institute of Technology Nagaland Nagaland Chumukedima Dimapur797 103 India

Identical video sharing across social networks results in redundant video data traffic, consuming additional bandwidth, storage resources, and data usage. A novel method is proposed to optimise redundant video-sharing services across social media applications exclusively on the push model, which is based on creating a unique hash value identity (UHVID) to identify the video distinctly. The UHVID is generated based on the centroid of gradient orientation of the key frames. The key frames are extracted from the video based on colour histogram and structural differences. Using UHVID and video metadata, the server application validates and optimizes identical video content flow before actual video sharing. The end users’ privacy is not affected in the proposed framework, as the actual video is not used for comparison, only the hash values of the video. The statistical analysis has shown that in 14 test cases of 2700 traffic initiations and 39,180 transactions, 83.2% of redundant data of identical videos were optimised, resulting in 63.61% data savings, amounting to 19.75 GB. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Digital storage

来源：评论

学校读者我要写书评

暂无评论

A systematic review on deep learning implementation in brain tumor segmentation, classification and prediction

引用

Multimedia Tools and Applications 2025年 1-40页

作者： Abid, Muhammad Adeel Munir, Kashif Institute of Computer Science Khwaja Fareed University of Engineering and Information Technology Rahim Yar Khan Pakistan Institute of Information Technology Khwaja Fareed University of Engineering and Information Technology Rahim Yar Khan Pakistan

The brain is the central part of the body that controls the overall functionality of the human body. The formulation of abnormal cells in the brain may lead to a brain tumor. Manual examination of a brain tumor is challenging and time-consuming. Deep learning is purely based on neural networks, and it's beneficial in identifying and diagnosing brain tumors. Different groups of researchers put efforts into implementing deep learning for the classification, segmentation, and prediction of brain tumor. They proposed different models of deep learning and the accuracy of their models in terms of dice score or evaluation parameters that are quite reasonable and acceptable. The primary purpose of this study is to review the different articles implementing deep learning in the segmentation, classification, and prediction of brain tumors. Appropriate keywords are used to extract articles from January 2018 to September 2023. Evaluation is done based on the implementation of deep learning and the accuracy of the proposed model, and a complete data sheet is maintained against each article. A total of 154 articles were collected, and 80 research articles were selected for this study after complete analysis. Each selected article proposed models based on deep learning with reasonable and acceptable accuracy in identifying the brain tumor. 2-D CNN and 3-D CNN are used on Magnetic Resonance Imaging (MRI) images of the brain for brain tumor detection. Thus, much more attention is required in the future on this topic to improve the accuracy of brain tumor identification. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Magnetic resonance imaging

来源：评论

学校读者我要写书评

暂无评论

Obtaining Crisp Priorities for Triangular and Trapezoidal Fuzzy Judgments

引用

computer Systems science & engineering 2022年第4期41卷 157-170页

作者： Raman Kumar Goyal Jaskirat Singh Nidhi Kalra Anshu Parashar Gagan Singla Sakshi Kaushal Computer Science and Engineering Department Thapar Institute of Engineering and TechnologyPatialaPunjabIndia Computer Science and Engineering University Institute of Engineering and TechnologyPanjab UniversityChandigarhIndia

This paper proposes anoptimal fuzzy-based model for obtaining crisp priorities for Fuzzy-AHP comparison *** judgments cannot be given for real-life situations,as most of these include some level of fuzziness and *** these situations,judgments are represented by the set of fuzzy *** of the fuzzy optimization models derive crisp priorities for judgments repre-sented with Triangular Fuzzy Numbers(TFNs)*** do not work for other types of Triangular Shaped Fuzzy Numbers(TSFNs)and Trapezoidal Fuzzy Numbers(TrFNs).To overcome this problem,a sum of squared error(SSE)based optimization model is *** some other methods,the proposed model derives crisp weights from all of the above-mentioned fuzzy judgments.A fuzzy number is simulated using the Monte Carlo method.A threshold-based constraint is also applied to minimize the deviation from the initial *** Algorithm(GA)is used to solve the optimization *** have also conducted casestudiesto show the proposed approach’s advantages over the *** show that the proposed model outperforms other models to minimize SSE and deviation from initial ***,the proposed model can be applied in various real time scenarios as it can reduce the SSE value upto 29%compared to the existing studies.

关键词： Analytic hierarchy process comparison matrices priority vectors fuzzy judgments triangular fuzzy numbers triangular-shaped fuzzy numbers trapezoidal fuzzy numbers

来源：评论

学校读者我要写书评

暂无评论

DD-SPP: Dynamic and Distributed Service Placement Policy for Optimal Scheduling in Fog-Edge Computing

引用

SN computer science 2024年第7期5卷 801页

作者： Kaur, Navjeet Bhardwaj, Vivek Department of Computer Science and Engineering Apex Institute of Technology Chandigarh University School of Computer Science and Engineering Manipal University Jaipur

The existing cloud model unable to handle abundant amount of Internet of Things (IoT) services placed by the end users due to its far distant location from end user and centralized nature. The edge and fog computing are the latest technologies for developing smart cities that are becoming popular to efficiently support latency-sensitive IoT application services. But these advanced technologies suffer from limited size and computation capabilities;therefore, optimal placement of services among available resources is an open issue. Therefore, the proposed work target the stated issue and presents a Dynamic and Distributed Service Placement Policy (DD-SPP) considering the edge and fog devices. The model is divided into three main phases, that is, the Service Type Estimator (STE), the Service Dependency Estimator & Resolution (SDER), and Resource Demand Estimator & Scheduling (RDES). STE and RDES modules depend on each other to perform optimal service placement. The results of the implementation showed satisfactory improvement with respect to present state-of-the-art policy where delay is improved by around 41%, energy is improved by 27%, response time by 28% and overall cost by 28%. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Cloud computing DD-SPP Edge computing Fog computing IoT Scheduling Service placement policy

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：