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An Efficient Automated Technique for Classification of Breast Cancer Using Deep Ensemble Model

computer Systems Science & engineering 2023年第7期46卷 897-911页

作者： Muhammad Zia Ur Rehman Jawad Ahmad Emad Sami Jaha Abdullah Marish Ali Mohammed A.Alzain Faisal Saeed Department of Electrical Engineering HITEC UniversityTaxila47080Pakistan School of Computing Edinburgh Napier UniversityEdinburghEH105DTUK Department of Computer Science Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Information Technology College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia DAAI Research Group Department of Computing and Data ScienceSchool of Computing and Digital TechnologyBirmingham City University15 Bartholomew RowBirminghamB55JUUK

Breast cancer is one of the leading cancers among *** has the second-highest mortality rate in women after lung *** detection,especially in the early stages,can help increase survival ***,manual diagnosis of breast cancer is a tedious and time-consuming process,and the accuracy of detection is reliant on the quality of the images and the radiologist’s ***,computer-aided medical diagnosis has recently shown promising results,leading to the need to develop an efficient system that can aid radiologists in diagnosing breast cancer in its early *** research presented in this paper is focused on the multi-class classification of breast *** deep transfer learning approach has been utilized to train the deep learning models,and a pre-processing technique has been used to improve the quality of the ultrasound *** proposed technique utilizes two deep learning models,Mobile-NetV2 and DenseNet201,for the composition of the deep ensemble *** learning models are fine-tuned along with hyperparameter tuning to achieve better ***,entropy-based feature selection is *** cancer identification using the proposed classification approach was found to attain an accuracy of 97.04%,while the sensitivity and F1 score were 96.87%and 96.76%,*** performance of the proposed model is very effective and outperforms other state-of-the-art techniques presented in the literature.

关键词： Breast cancer image enhancement ensemble model transfer learning feature selection

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Sustainable Learning of computer Programming Languages Using Mind Mapping

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Intelligent Automation & Soft Computing 2023年第5期36卷 1687-1697页

作者： Shahla Gul Muhammad Asif Zubair Nawaz Muhammad Haris Aziz Shahzada Khurram Muhammad Qaiser Saleem Elturabi Osman Ahmed Habib Muhammad Shafiq Osama E.Sheta Department of Computer Science National Textile University Faisalabad37610Pakistan Department of Data Science University of the PunjabLahore54590Pakistan Department of Mechanical Engineering University of SargodhaSargodha40100Pakistan Faculty of Computing The Islamia University of BahawalpurBahawalpur63100Pakistan College of Computer Science and Information Technology Al Baha UniversityAl Baha1988Saudi Arabia Department of Information and Communication Engineering Yeungnam UniversityGyeongsan38541Korea College of Science Zagazig UniversityZagazig44511Egypt

In the current era of information technology,students need to learn modern programming languages efﬁ*** art of teaching/learning program-ming requires many logical and conceptual *** it’s a challenging task for the instructors/learners to teach/learn these programming languages effectively and efﬁ*** mapping is a useful visual tool for establishing ideas and connecting them to solve *** research proposed an effective way to teach programming languages through visual *** experimental study uses a mind mapping tool to teach two programming environments:Text-based Programming and Blocks-based *** performed the experiments with one hundred and sixty undergraduate students of two public sector universities in the Asia Paciﬁc *** different instructional approaches,including block-based language(BBL),text-based languages(TBL),mind map with text-based language(MMTBL)and mind mapping with block-based(MMBBL)are used for this *** results show that instructional approaches using a mind mapping tool to help students solve given tasks in their critical thinking are more effective than other instructional techniques.

关键词： Text programming blocks programming novice programmer

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A research of MPLS-based network fault recovery

A research of MPLS-based network fault recovery

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International Conference on Intelligent Network and Intelligent Systems

作者： Qiu, Yimin Zhu, Hongbing Zhou, Yi Gu, Jinguang Computer School Wuhan University Wuhan China College of Computer Science and Technology Wuhan University of Science and Technology Wuhan China Faculty of Information Design Hiroshima Kokusai Gakuin University Hiroshima Japan WISDRI Engineering and Research Incorporation Limited Wuhan China Wuhan University of Science and Technology Wuhan China

ISBN: (纸本)9780769542492

Researching on use of new real-time connection-oriented services like streaming technologies and mission-critical transaction-oriented services and more reliable network become inevitable trends presently. MPLS is a next generation backbone architecture, it can speed up packet forwarding to destination by label switching. However, if there exists no backup LSP when the primary LSP fails, MPLS frames cannot be forwarded to destination. Therefore, fault recovery has become an important research area in MPLS Traffic engineering. At present, two famous methods, Makam and Haskin are belonging to Protection Switching, and other methods basically come into being on base of them. But these two famous methods both have its disadvantage. In order to solve their drawback, the thesis tries to do some exploration on the MPLS-based recovery model. This new model in the thesis using the Reverse Backup Path to solve the wrapping of data back to the path is too long, and using the simulation tool NS-2 to do some experiments. Finally the simulation experiments show that new method of MPLS-based recovery has less packet disorder and much lower delay and packet losses than the two famous methods. © 2010 IEEE.

关键词： computer system recovery

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Latency-Minimized Computation Offloading in Vehicle Fog Computing with Improved Whale Optimization Algorithm

Latency-Minimized Computation Offloading in Vehicle Fog Comp...

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2023 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2023

作者： Bi, Jing Xue, Xiangdong Yuan, Haitao Zhang, Jia Beijing University of Technology Faculty of Information Technology Beijing100124 China School of Automation Science and Electrical Engineering Beihang University Beijing100191 China Southern Methodist University Dept. of Computer Science Lyle School of Engineering DallasTX United States

ISBN: (纸本)9798350337020

Fog computing provides lower latency and higher bandwidth compared to cloud computing and is widely used in Internet of Vehicles (IoV). Vehicles cannot compute all tasks locally due to their limited computing power and battery capacity. Thus, it is a useful way to offload some tasks of vehicles to other resource-rich servers. However, due to the high mobility of vehicles, there may be a failure of returning computing results. Thus, it is a challenge to minimize the latency of tasks while meeting the constraint of energy consumption. This work proposes a vehicle-fog offloading system that offloads tasks to fog servers or idle vehicles which is a probabilistic offloading problem. So this work proposes an improved optimization algorithm called an adaptive Lévy flight-based Whale optimization algorithm with Hierarchical learning (LWH) to solve this problem. Simulation experiments show that LWH has strong global search capability and outperforms its five typical and widely used algorithms. © 2023 IEEE.

关键词： computation offloading fog computing Lévy flight Vehicular networks whale optimization algorithm

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Design of the neural processor optimized for Xilinx Virtex FPGA devices

Design of the neural processor optimized for Xilinx Virtex F...

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International Conference on information technology Interfaces

作者： J. Hudec M. Hust'ava Faculty of Electrical Engineering and Information Technology Department of Computer Science and Engineering Slovak University of Technology Bratislava Slovakia

ISBN: (纸本)9539676932

Focuses on the design of a parallel processor targeted at the rapid execution of neural networks. The basic architecture of the toroidal neural processor (TNP) is based on a toroidal mesh. This architecture was inspired by the need for a low-cost massively parallel processing system that could emulate a large variety of neural models. The TNP consists of two basic elements: a control unit and some processing units. The control unit acts as distributor of information and instructions for the processing units. The processing units perform exact operations on the data, based on the execution of instructions. The design of the TNP has a typical SIMD architecture. The processor has an enhanced interface with the host computer. This interface provides not only operations for programming and control of the TNP, but, in addition, any type of neural network and learning algorithm can be implemented through this interface. In the design of the TNP are implemented 10 control unit instructions and 11 processing unit instructions. The architecture of the TNP is optimized for Xilinx Virtex devices. The design uses many features of this family of FPGA devices. The VHDL constructs are mapped into hardware in the synthesis, optimization, place-and-route and implementation process. The optimization can significantly change the hardware that is generated. The TNP was tested, simulated and implemented in a Xilinx Foundation technology Express version 3.3i environment with the Virtex XCV300 FPGA array and the HW-AFX-BG352-100 prototyping platform. The whole design can be implemented in Virtex E and Spartan devices too.

关键词： Design optimization Field programmable gate arrays Algorithm design and analysis information technology Process design Neural networks Pipelines Design engineering Parallel processing computer architecture

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SOFSEM 2006: Theory and Practice of computer Science 1

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丛书名： Lecture Notes in computer Science

1000年

作者： Jiří Wiedermann Július Štuller Gerard Tel Jaroslav Pokorný Mária Bieliková

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Real Time Early-stage Influenza Detection with Emotion Factors from Sina Microblog 5

Real Time Early-stage Influenza Detection with Emotion Facto...

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5th Workshop on South and Southeast Asian NLP, WSSANLP 2014

作者： Sun, Xiao Ye, Jiaqi Ren, Fuji School of Computer and Information Hefei University of Technology Anhui Hefei China Anhui Province Key Laboratory of Affective Computing and Advanced Intelligent Machine China Faculty of Engineering University of Tokushima Tokushima Japan

ISBN: (纸本)9781873769416

Influenza is an acute respiratory illness that occurs every year. Detection of Influenza in its earliest stage would reduce the spread of the illness. Sina microblog is a popular microblogging service, provides perfect sources for flu detection due to its real-time nature and large number of users. In this paper we investigate the real-time flu detection problem and describe a Flu model with emotion factors and sematic information (em-flu model). Experimental results show the robustness and effectiveness of our method and we are hopeful that it would help health organizations in identifying flu outbreak and take timely actions to control. © 2014 Proceedings of the Conference - 5th Workshop on South and Southeast Asian NLP, WSSANLP 2014 - co-located with the 25th International Conference on Computational Linguistics, COLING 2014. All rights reserved.

关键词： Diseases

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Web Traffic Anomaly Detection Using a Hybrid Spatio-temporal Neural Network

Web Traffic Anomaly Detection Using a Hybrid Spatio-temporal...

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2023 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2023

作者： Bi, Jing Xu, Lifeng Yuan, Haitao Zhang, Jia Beijing University of Technology Faculty of Information Technology Beijing100124 China School of Automation Science and Electrical Engineering Beihang University Beijing100191 China School of Engineering Dept. of Computer Science in Lyle Southern Methodist University DallasTX United States

ISBN: (纸本)9798350337020

Nowadays, rapid development of Internet has brought a sharp increase in traffic data. Abnormal traffic haS serious impact on network security. Traffic anomaly detection can be achieved by extracting characteristics of network traffic to detect anomalous intrusions, and therefore, anomaly detection algorithms are of great significance to maintenance of network security. This work proposes a hybrid spatio-temporal neural network with attention named CTGA to effectively identify anomalous traffic. CTGA combines a Convolutional neural network (CNN), a Temporal convolutional network (TCN), a bidirectional Gated recurrent unit network (BiGRU), and a self-Attention mechanism. It automatically extracts temporal and spatial features of sequences from raw data by sliding window preprocessing followed by CNN, TCN, BiGRU, and the self-attention mechanism to detect anomalous data. CNN is used to extract spatial features of time sequences and reduce the loss of spatial information. In the sequence, TCN obtains short-term features. Long-term dependencies in the data are captured by BiGRU, and the self-attention mechanism obtains important information in the sequence. Finally, experiments with the real-life Yahoo S5 dataset prove that CTGA outperforms other approaches substantially. © 2023 IEEE.

关键词： Anomaly detection bidirectional gated recurrent unit convolutional neural network self-attention temporal convolutional network

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A New Method for Detecting Altered Text in Document Images 2nd

A New Method for Detecting Altered Text in Document Images

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2nd International Conference on Pattern Recognition and Artificial Intelligence, ICPRAI 2020

作者： Nandanwar, Lokesh Shivakumara, Palaiahnakote Pal, Umapada Lu, Tong Lopresti, Daniel Seraogi, Bhagesh Chaudhuri, Bidyut B. Faculty of Computer Science and Information Technology University of Malaya Kuala Lumpur Malaysia Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India National Key Lab for Novel Software Technology Nanjing University Nanjing China Computer Science and Engineering Lehigh University BethlehemPA United States

ISBN: (纸本)9783030598297

As more and more office documents are captured, stored, and shared in digital format, and as image editing software becomes increasingly more powerful, there is a growing concern about document authenticity. For example, texts in property documents can be altered to make an illegal deal, or the date on an airline ticket can be altered to gain entry to airport terminals by breaching security. To prevent such illicit activities, this paper presents a new method for detecting altered text in a document. The proposed method explores the relationship between positive and negative coefficients of a DCT to extract the effect of distortions caused by tampering operations. Here we divide DCT coefficients into positive and negative classes, then reconstructs images from the inverse DCT of the respective positive and negative coefficients. Next, we perform Laplacian filtering over reconstructed images for widening the gap between the values of text and other pixels. Then filtered images of positive and negative coefficients are fused by an average operation. For a fused image, we generate Canny and Sobel edge images in order to investigate the effect of distortion through quality measures, namely, MSE, PSNR and SSIM used as features. In addition, for the fused image, the proposed method extracts features based on histograms over the residual images. The features are then passed on to a deep Convolutional Neural Network for classification. The proposed method is tested on our own dataset as well as two standard datasets, namely IMEI and the ICPR 2018 Fraud Contest dataset. The results show that the proposed method is effective and outperforms existing methods. © 2020, Springer Nature Switzerland AG.

关键词： Inverse problems

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Embedded SRAM trend in nano-scale CMOS

Embedded SRAM trend in nano-scale CMOS

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IEEE International Workshop on Memory technology, Design and Testing

作者： Hiroyuki Yamauchi Faculty of Information Engineering Department of Computer Science and Engineering Fukuoka Institute of Technology Fukuoka Japan

This paper describes an SRAM scaling trend in terms of bit-cell size and operating voltage (Vdd) in a nano-scale process generation. The key design solutions to extend a 6T SRAM lifetime are reviewed and discussed including a possible bit cell scaling trend comparing with an 8T SRAM as one of the successors. Each dependency of 3 key margins of write margin (WRM), static noise margin (SNM), and cell current (Icell) on the scaling ratio of Vdd and MOSFET channel feature size has been shown to clarify the real issues in the scaling. The bit cell area scaling trends of 6T and 8T SRAMs are predicted. It has been shown that the area of 6T will be getting closer to that of 8T at 32 nm and should cross over around 22 nm.

关键词： Random access memory MOS devices Voltage CMOS process computer science CMOS technology Signal to noise ratio MOSFET circuits Topology Very large scale integration

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