检索结果-内蒙古大学图书馆

Genetics Based Compact Fuzzy System for Visual Sensor Network

computer Systems science & engineering 2023年第4期45卷 409-426页

作者： Usama Abdur Rahman C.Jayakumar Deepak Dahiya C.R.Rene Robin Department of Computer Science and Engineering Jerusalem College of EngineeringChennaiTamil Nadu600100India Department of Computer Science and Engineering Sri Venkateswara College of EngineeringChennaiTamil Nadu602117India College of Computer and Information Sciences(CCIS) Majmaah UniversityMajmaah11952Kingdom of Saudi Arabia Department of Computer Science and Engineering Sri Sairam Engineering CollegeChennai600044India

As a component of Wireless Sensor Network(WSN),Visual-WSN(VWSN)utilizes cameras to obtain relevant data including visual recordings and static *** from the camera is sent to energy efficient sink to extract key-information out of *** applications range from health care monitoring to military *** a network with VWSN,there are multiple challenges to move high volume data from a source location to a target and the key challenges include energy,memory and I/O *** this case,Mobile Sinks(MS)can be employed for data collection which not only collects information from particular chosen nodes called Cluster Head(CH),it also collects data from nearby nodes as *** innovation of our work is to intelligently decide on a particular node as CH whose selection criteria would directly have an impact on QoS parameters of the ***,making an appropriate choice during CH selection is a daunting task as the dynamic and mobile nature of MSs has to be taken into *** propose Genetic Machine Learning based Fuzzy system for clustering which has the potential to simulate human cognitive behavior to observe,learn and understand things from manual *** architecture is designed based on Mamdani’s fuzzy *** parameters are derived based on the model residual energy,node centrality,distance between the sink and current position,node centrality,node density,node history,and mobility of sink as input variables for decision making in CH *** inputs received have a direct impact on the Fuzzy logic rules mechanism which in turn affects the accuracy of *** proposed work creates a mechanism to learn the fuzzy rules using Genetic Algorithm(GA)and to optimize the fuzzy rules base in order to eliminate irrelevant and repetitive *** algorithmbased machine learning optimizes the interpretability aspect of fuzzy *** results are obtained using *** result shows that the classification acc

关键词： Visual sensor network fuzzy system genetic based machine learning mobile sink efficient energy life of network

来源：评论

学校读者我要写书评

暂无评论

U-YOLOv3: A Model Focused on Underwater Object Detection

引用

Informatica (Slovenia) 2025年第6期49卷 87-102页

作者： Sarkar, Pratima De, Sourav Gurung, Sandeep Department of Computer Science and Engineering Sikkim Manipal Institute of Technology Majhitar India Sikkim Manipal University Gangtok India Department of Computer Science and Engineering Techno International New Town Newtown Kolkata India Department of Computer Science and Engineering Cooch Behar Government Engineering College Vill-Harinchowrah West Bengal Cooch Behar736170 India

Underwater image enhancement and object detection has great potential for studying underwater environments. It has been utilized in various domains, including image-based underwater monitoring and Autonomous Underwater Vehicle (AUV)-driven applications such as underwater terrain surveying. It has been observed that underwater images are not clear due to several factors such as low light, the presence of small particles, different levels of refraction of light, etc. Extracting high-quality features from these images to detect objects is a significant challenging task. To mitigate this challenge, MIRNet and the modified version of YOLOv3 namely Underwater-YOLOv3 (U-YOLOv3) is proposed. The MIRNet is a deep learning-based technology for enhancing underwater images. while using YOLOv3 for underwater object detection it lacks in detection of very small objects and huge-size objects. To address this problem proper anchor box size, quality feature aggregation technique, and during object classification image resizing is required. The proposed U-YOLOv3 has three unique features that help to work with the above specified issue like accurate anchor box determination using the K-means++ clustering algorithm, introduced Spatial Pyramid Pooling (SPP) layer during feature extraction which helps in feature aggregation, and added downsampling and upsampling to improve the detection rate of very large and very small size objects. The size of the anchor box is crucial in detecting objects of different sizes, SPP helps in aggregation of features, while down and upsampling changes sizes of objects during object detection. Precision, recall, F1-score and mAP are used as assessment metrics to assess proposed work. The proposed work compared with SSD, Tiny-YOLO, YOLOv2, YOLOv3, YOLOv4, YOLOv5, KPE-YOLOv5, YOLOv7, YOLOv8 and YOLOv9 single stage object detectors. The experiment on the Brackish and Trash ICRA19 datasets shows that our proposed method enhances the mean average precision for b

关键词： Underwater imaging

来源：评论

学校读者我要写书评

暂无评论

An Ophthalmic Evaluation of Central Serous Chorioretinopathy

引用

computer Systems science & engineering 2023年第1期44卷 613-628页

作者： L.K.Shoba P.Mohan Kumar Department of Information Technology Jeppiaar Engineering CollegeChennai600119India Department of Computer Science and Engineering Sri Krishna College of Engineering and TechnologyCoimbatore641008India

Nowadays in the medicalfield,imaging techniques such as Optical Coherence Tomography(OCT)are mainly used to identify retinal *** this paper,the Central Serous Chorio Retinopathy(CSCR)image is analyzed for various stages and then compares the difference between CSCR before as well as after treatment using different application *** approach,which was focused on image quality,improves medical image *** enhancement algorithm was implemented to improve the OCT image contrast and denoise purpose called Boosted Anisotropic Diffusion with an Unsharp Masking Filter(BADWUMF).The classifier used here is tofigure out whether the OCT image is a CSCR case or not.150 images are checked for this research work(75 abnormal from Optical Coherence Tomography Image Retinal Database,in-house clinical database,and 75 normal images).This article explicitly decides that the approaches suggested aid the ophthalmologist with the precise retinal analysis and hence the risk factors to be *** total precision is 90 percent obtained from the Two Class Support Vector Machine(TCSVM)classifier and 93.3 percent is obtained from Shallow Neural Network with the Powell-Beale(SNNWPB)classifier using the MATLAB 2019a program.

关键词： OCT CSCR macula segmentation boosted anisotropic diffusion with unsharp maskingfilter two class support vector machine classifier and shallow neural network with powell-beale classifier

来源：评论

学校读者我要写书评

暂无评论

Automatic lung cancer detection using hybrid particle snake swarm optimization with optimized mask RCNN

引用

Multimedia Tools and Applications 2024年第31期83卷 76807-76831页

作者： Sudha, R. Maheswari, K. M. Uma Department of Computer Science and Engineering School of computing College of engineering and technology SRM Institute of Science and Technology Kattankulathur India Department of Computing Technologies School of Computing College of engineering and technology SRM Institute of Science and Technology Kattankulathur India

As a result of its aggressive nature and late identification at advanced stages, lung cancer is one of the leading causes of cancer-related deaths. Lung cancer early diagnosis is a serious and difficult challenge that is crucial to a person's survival. The first diagnosis of the malignant nodules is typically made using chest radiography (X-rays) and computed tomography (CT) scans;however, the potential presence of benign nodules results in incorrect conclusions. The early phases of both benign and malignant nodules exhibit striking similarities. In this paper, a novel deep learning-based model is proposed for the precise diagnosis of malignant nodules. The proposed approach consists of two stages namely, pre-processing and lung nodule detection. Initially, the Lung CT scan images are collected from the dataset. Then, to remove the noise present in the input image, we apply an adaptive median filter. Then, to enhance the image, Contrast Limited Adaptive Histogram Equalization (CLAHE) is applied. After pre-processing, the image is given to the optimized mask RCNN classifier to detect the malignant and benign nodules. To enhance the performance of the Mask RCNN classifier, the hyper-parameters are optimally selected using hybrid particle snake swarm optimization (PS2OA). The proposed PS2OA is a hybridization of particle swarm optimization (PSO) and snake swarm optimization (SSO). The performance of the proposed approach is analyzed based on different metrics and effectiveness compared with state-of-the-art works. The proposed approach attained the maximum accuracy of 97.67%. This work aimed at assisting radiologists to detect and diagnose small-size pulmonary nodules more accurately. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Diagnosis

来源：评论

学校读者我要写书评

暂无评论

Finite State Automata Based Cryptosystem for Secure Data Sharing and De-duplication in Cloud Computing

引用

SN computer science 2024年第6期5卷 774页

作者： Kodada, Basappa B. D’Mello, Demian Antony Kumar, D. K. Santhosh Department of AIML Canara Engineering College Affiliated to VTU Belagavi Department of Computer Science and Engineering Canara Engineering College Affiliated to VTU Belagavi Department of Computer Science and Engineering St. Joseph Engineering College Affiliated to VTU Belagavi

In the era of high-speed internet access, a surge in redundant data generation is observed across different media sources and devices, posing challenges in computational and storage efficiency during data outsourcing and retrieval. Addressing this issue and optimizing storage efficiency is essential to curtailing redundant data creation within storage servers. The potential compromise of sensitive data within outsourced information is susceptible to both internal and external threats. Thus, securing data during both data-in-transmission and data-at-storage is imperative. However, conventional cryptosystems alongside deduplication services face a dilemma due to the conflict between encryption and deduplication. Furthermore, prevailing cryptosystems pose false keys and false ownership claiming, bruit-force, and dictionary attacks during the secure deduplication process. To tackle these challenges, this paper utilizes a Finite State Automata (FSA) based cryptosystem alongside deduplication, complemented by Proof of Ownership (PoW) and Data Integrity Verification (DIV) protocols. This method ensures robust data security effectively by resolving the encryption-deduplication paradox. Empirical evaluation validates the efficacy of the proposed method, showcasing improvements over the existing system. It also demonstrates reduced communication and computational complexity and improved storage efficiency, and fortified security provisions during data deduplication. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Data confidentiality Data integrity Deduplication Encryption Finite state machine Privacy Secure data sharing Security

来源：评论

学校读者我要写书评

暂无评论

Implementation and Development of Static Hand gesture recognition in a real time using Diagonal Sum algorithm for Gestural Language 2

Implementation and Development of Static Hand gesture recogn...

引用

2nd IEEE International Conference on Emerging Research in Computational science, ICERCS 2024

作者： Jemin, V.M. Sasikumar, R. RMK College of Engineering and Technology Department of Computer Science and Engineering Thiruvallur India RMD Engineering College Department of Computer Science and Engineering Thiruvallur India

ISBN: (纸本)9798331534950

This manuscript deems the proposal over utilization of computer digitized vision over the gesture recognition. Gesture language is a language that determines the requirement over combining the finger gesture, its orientation, arm and hand movement, facial & body expression that simultaneously explores and advertises the people thoughts. The digital camera makes the recording of live motion streams of pictures with which the acquisition of image is made with the assistance of interface. The training of system is made over each sort of Figureureureureure gestures as representing (5,4,3,2 or 1) atleast in a single time. Later on, the test symbol is delivered and the system makes a try for detecting it. In this proposed study the detection of Figureureureureure gestures is made using the strategy of image processing. The system makes the detection of cumulative finger count. Later on, it makes the identification of individual fingers above the palm. During the processing, it initially makes the detection of skin tone (Color) from the acquired image by the utilization of filter. The image is allowed to process through subsequent steps in order to depict the correct count of fingers. The model makes the detection over the nearer point from the threshold value. The detection of image is made as per the centroid value. Later on, the implication of certain steps is made for enhancing the normal image to an efficient image so that the exposure of fingers is made. Finally, the model makes the detection and decides the finger count and advertises the calculation to the tester. As a result, the classification is done using artificial neural networks based on the previously formulated training model that has been built and realized with more than 92.5% of accuracy in the finger gesture recognition. In this study, the comparison has also been enumerated with other state of art algorithms designed by many researchers. The classification has been illustrated with diagonal sum algori

关键词： Binary images

来源：评论

学校读者我要写书评

暂无评论

Music to Score Conversion using Machine Learning 4

Music to Score Conversion using Machine Learning

引用

4th IEEE International Conference on Data engineering and Communication Systems, ICDECS 2024

作者： Julian, Anitha Mukund, Vibha Saveetha Engineering College Department of Computer Science and Engineering Chennai India Saveetha Engineering College Department of Computer Science Chennai India

ISBN: (纸本)9798350393354

The Music to Score Conversion (MSC) project focuses on bridging the gap between auditory and visual representations of music. It uses signal processing techniques for the conversion such as pitch estimation, onset detection, fourier transform and Constant Q Transform. The study also explores real-time conversion for music education and production, as well as strategies to increase accessibility for individuals with hearing impairments. Rigorous testing on diverse datasets demonstrates the potential of these methodologies to revolutionize various aspects of the music industry. This research not only advances MSC but also offers crucial insights into its transformative impact on musical experiences and inclusivity across diverse audiences and sectors, enabling seamless translation from auditory to visual musical representations. © 2024 IEEE.

关键词： Music

来源：评论

学校读者我要写书评

暂无评论

An Advanced Dynamic Scheduling for Achieving Optimal Resource Allocation

引用

computer Systems science & engineering 2023年第1期44卷 281-295页

作者： R.Prabhu S.Rajesh Department of Computer Science and Engineering AAA College of Engineering and TechnologySivakasiTamilnaduIndia Department of Information Technology Mepco Schlenk Engineering College SivakasiTamilnadu India

Cloud computing distributes task-parallel among the various *** with self-service supported and on-demand service have rapid *** these applications,cloud computing allocates the resources dynami-cally via the internet according to user *** resource allocation is vital for fulfilling user *** contrast,improper resource allocations result to load imbalance,which leads to severe service *** cloud resources implement internet-connected devices using the protocols for storing,communi-cating,and *** extensive needs and lack of optimal resource allo-cating scheme make cloud computing more *** paper proposes an NMDS(Network Manager based Dynamic Scheduling)for achieving a prominent resource allocation scheme for the *** proposed system mainly focuses on dimensionality problems,where the conventional methods fail to address *** proposed system introduced three–threshold mode of task based on its size STT,MTT,LTT(small,medium,large task thresholding).Along with it,task mer-ging enables minimum energy consumption and response *** proposed NMDS is compared with the existing Energy-efficient Dynamic Scheduling scheme(EDS)and Decentralized Virtual Machine Migration(DVM).With a Network Manager-based Dynamic Scheduling,the proposed model achieves excellence in resource allocation compared to the other existing *** obtained results shows the proposed system effectively allocate the resources and achieves about 94%of energy efficient than the other *** evaluation metrics taken for comparison are energy consumption,mean response time,percentage of resource utilization,and migration.

关键词： Cloud computing resource allocation load balance dynamic scheduling dimensionality reduction

来源：评论

学校读者我要写书评

暂无评论

Optimal Deep Belief Network Based Lung Cancer Detection and Survival Rate Prediction

引用

computer Systems science & engineering 2023年第4期45卷 939-953页

作者： Sindhuja Manickavasagam Poonkuzhali Sugumaran Department of Information Technology Rajalakshmi Engineering CollegeChennai600125TamilnaduIndia Department of Computer Science and Engineering Rajalakshmi Engineering CollegeChennai600125TamilnaduIndia

The combination of machine learning(ML)approaches in healthcare is a massive advantage designed at curing illness of millions of *** efforts are used by researchers for detecting and providing primary phase insights as to cancer *** cancer remained the essential source of disease connected mortality for both men as well as women and their frequency was increasing around the *** disease is the unrestrained progress of irregular cells which begin off in one or both *** previous detection of cancer is not simpler procedure however if it can be detected,it can be curable,also finding the survival rate is a major challenging *** study develops an Ant lion Optimization(ALO)with Deep Belief Network(DBN)for Lung Cancer Detection and Classification with survival rate *** proposed model aims to identify and classify the presence of lung ***,the proposed model undergoes min-max data normalization approach to preprocess the input ***,the ALO algorithm gets executed to choose an optimal subset of *** addition,the DBN model receives the chosen features and performs lung cancer ***,the optimizer is utilized for hyperparameter optimization of the DBN *** order to report the enhanced performance of the proposed model,a wide-ranging experimental analysis is performed and the results reported the supremacy of the proposed model.

关键词： Lung cancer feature selection ant lion optimization classification disease diagnosis metaheuristics

来源：评论

学校读者我要写书评

暂无评论

Deep ensemble model for skin cancer classification with improved feature set

引用

Multimedia Tools and Applications 2025年第10期84卷 7599-7626页

作者： Burada, Sreedhar Manjunathswamy, B.E. Kumar, M. Sunil Department of Computer Science and Engineering Don Bosco Institute of Technology Karnataka Bengaluru560074 India Department of Computer Science and Engineering School of Computing Mohan Babu University Sree Vidyanikethan Engineering College Andhra Pradesh Tirupati517102 India

Early identification of skin cancer is mandatory to minimize the worldwide death rate as this disease is covering more than 30% of mortality rates in young and adults. Researchers are in the move of proposing advanced models for the accurate classification of skin disease. This paper introduces a novel skin cancer detection model under the stages of preprocessing, feature extraction, classification as well as segmentation. The given input is first preprocessed via Gaussian filtering. Subsequently, the Deep learning model termed as ‘U-net’ is used for the segmentation process. Afterward, the extraction of the feature set including features like Improved Center symmetric Local Binary Pattern (ICS-LBP), Local Gradient Pattern (LGP), Median binary pattern (MBP) is performed. Classification takes place using Deep Ensemble Model based on the extracted features, that includes BiGRU, Deep Maxout, and Improved Deep Convolutional Neural Network (CNN), respectively. Also, as the hyperparameter tuning of the classification model enhances the performance, this work introduces a new Hybrid Atom and Arithmetic Operation Algorithm (HAAOA) algorithm for tuning the optimal weights of the Bi-GRU (Bi-directional Gated Recurrent Unit) and Deep Maxout model. At last, the validation of the proposed work is performed through different performance measures by comparing it with different algorithms. Accordingly, accuracy of the adopted method at 90th learning percentage is 93.56%, which is (~) 82.63%, 85.15%, 84.98%, 83.66% and 83.42% better than AOA, ASO, BWO, BMO and DO, 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

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：