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Reinforcing Artificial Neural Networks through Traditional Machine Learning Algorithms for Robust Classification of Cancer

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computers, Materials & Continua 2023年第5期75卷 4293-4315页

作者： Muhammad Hammad Waseem Malik Sajjad Ahmed Nadeem Ishtiaq Rasool Khan Seong-O-Shim Wajid Aziz Usman Habib Department of Computer Sciences and Information Technology University of Azad Jammu and KashmirMuzaffarabad13100Pakistan Department of Computer Science and AI College of Computer Science and EngineeringUniversity of JeddahJeddah23890Saudi Arabia Department of Computer and Network Engineering College of Computer Science and EngineeringUniversity of JeddahJeddah23890Saudi Arabia Department of AI&DS School of ComputingNational University of Computer and Emerging SciencesIslamabad44000Pakistan

Machine Learning(ML)-based prediction and classification systems employ data and learning algorithms to forecast target ***,improving predictive accuracy is a crucial step for informed *** the healthcare domain,data are available in the form of genetic profiles and clinical characteristics to build prediction models for complex tasks like cancer detection or *** ML algorithms,Artificial Neural Networks(ANNs)are considered the most suitable framework for many classification *** network weights and the activation functions are the two crucial elements in the learning process of an *** weights affect the prediction ability and the convergence efficiency of the *** traditional settings,ANNs assign random weights to the *** research aims to develop a learning system for reliable cancer prediction by initializing more realistic weights computed using a supervised setting instead of random *** proposed learning system uses hybrid and traditional machine learning techniques such as Support Vector Machine(SVM),Linear Discriminant Analysis(LDA),Random Forest(RF),k-Nearest Neighbour(kNN),and ANN to achieve better accuracy in colon and breast cancer *** system computes the confusion matrix-based metrics for traditional and proposed *** proposed framework attains the highest accuracy of 89.24 percent using the colon cancer dataset and 72.20 percent using the breast cancer dataset,which outperforms the other *** results show that the proposed learning system has higher predictive accuracies than conventional classifiers for each dataset,overcoming previous research ***,the proposed framework is of use to predict and classify cancer patients ***,this will facilitate the effective management of cancer patients.

关键词： ANN decision support systems gene-expression data hybrid classification machine learning predictive analytics

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Performance Evaluation of Biometric Authentication Using Fragment Jaya Optimizer-Based Deep CNN with Multi-kernel SVM

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SN computer science 2024年第4期5卷 337页

作者： Umasankari, N. Muthukumar, B. Shanmuganathan, C. Department of Computer Science and Engineering Sathyabama Institute of Science and Technology Chennai India School of Computing Department of CSE Sathyabama Institute of Science and Technology Chennai India Rajalakshmi Engineering College Chennai India Department of Computer Science and Engineering SRM Institute of Science and Technology Ramapuram Chennai India

The earlier research clearly indicated that the bimodal authentication system has more efficiency than unimodal and multimodal. This is due to the reason for the best intact biometric traits of fingerprint and retina. There is a chance to additionally improve further performance of the proposed biometric trait combination by additional or alternate algorithms or methodologies. Therefore, in this research work, the multi-kernel support vector machine (MK-SVM), a machine learning classification approach, is proposed and is used for the implementation. In addition, a hybrid algorithm of fragment Jaya optimizer-based deep convolutional neural network (FJO-DCNN) approach is also used to improve the performance value for bimodal biometric authentication and classification. The recognition systems analyze both biometrics independently, and their conclusions are combined to determine whether to give or refuse access to the user in the end. According to the findings of the implementation, this work demonstrates more dependability than the cascaded biometric system. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd 2024.

关键词： Bimodal biometric authentication Deep convolutional neural network Fragment Jaya optimizer convolutional neural network Multi-kernel support vector machine

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Scheduling a Single-Arm Multi-Cluster Tool With a Condition-Based Cleaning Operation

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IEEE/CAA Journal of Automatica Sinica 2023年第10期10卷 1965-1983页

作者： Qinghua Zhu Hongpeng Li Cong Wang Yan Hou School of Computer Science and Technology Guangdong University of TechnologyGuangzhou 510006China Department of Electrical and Computer Engineering New Jersey Institute of TechnologyNewarkNJ 07102-1982 USA

As wafer circuit widths shrink less than 10 nm,stringent quality control is imposed on the wafer fabrication processes. Therefore, wafer residency time constraints and chamber cleaning operations are widely required in chemical vapor deposition, coating processes, etc. They increase scheduling complexity in cluster tools. In this paper, we focus on scheduling single-arm multi-cluster tools with chamber cleaning operations subject to wafer residency time constraints. When a chamber is being cleaned, it can be viewed as processing a virtual wafer. In this way, chamber cleaning operations can be performed while wafer residency time constraints for real wafers are not violated. Based on such a method, we present the necessary and sufficient conditions to analytically check whether a single-arm multi-cluster tool can be scheduled with a chamber cleaning operation and wafer residency time constraints. An algorithm is proposed to adjust the cycle time for a cleaning operation that lasts a long cleaning ***, algorithms for a feasible schedule are also *** an algorithm is presented for operating a multi-cluster tool back to a steady state after the cleaning. Illustrative examples are given to show the application and effectiveness of the proposed method.

关键词： Chamber cleaning multi-cluster tools scheduling semiconductor manufacturing

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A Parallel Color Image Encryption Algorithm Based on a 2-D Logistic-Rulkov Neuron Map

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IEEE Internet of Things Journal 2025年第11期12卷 18115-18124页

作者： Gao, Suo Zhang, Zheyi Iu, Herbert Ho-Ching Ding, Siqi Mou, Jun Erkan, Uğur Toktas, Abdurrahim Li, Qi Wang, Chunpeng Cao, Yinghong Dalian Polytechnic University School of Information Science and Engineering Dalian116034 China The University of Western Australia School of Electrical Electronic and Computer Engineering PerthWA6009 Australia Dalian University of Technology School of Mathematical Sciences Dalian116024 China Karamanoglu Mehmetbey University Faculty of Engineering Department of Computer Engineering Karaman70200 Turkey Ankara University Engineering Faculty Department of Artificial Intelligence and Data Engineering Ankara06830 Turkey Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan250000 China

Images are widely used in social networks, necessitating efficient and secure transmission, especially in bandwidth-constrained environments. This article aims to develop a color image encryption algorithm that enhances security while optimizing computational efficiency. A novel parallel color image encryption algorithm based on the 2-D logistic-Rulkov neuron map (2D-LRNM) is proposed. In this approach, the three channels of the color image are first separated. Cross-channel information interaction is introduced to form three new channels, which are then processed in parallel. During the encryption process of each channel, a block-wise parallel encryption mechanism is applied, ensuring simultaneous encryption of each block. This block-wise strategy effectively leverages parallel computing resources and balances the task load. To meet the demand for a large number of keystreams during encryption, the 2D-LRNM is introduced. It combines the simplicity and chaotic properties of the Logistic map with the multitimescale dynamics and neurodynamic behaviors of the Rulkov map. By overcoming the dimensional limitations inherent in the single Logistic map, this approach extends the system to a 2-D framework, significantly increasing the complexity of chaotic behavior and improving its unpredictability. Experimental results demonstrate that the proposed encryption algorithm achieves high security and reduces computation time by approximately 83.3%. © 2014 IEEE.

关键词： Encryption algorithms

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Multi-Factor Authentication System With ID Card Credentials For Secure Transactions 14

Multi-Factor Authentication System With ID Card Credentials ...

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14th International Conference on computing Communication and Networking Technologies, ICCCNT 2023

作者： Vasudev, R. Harini, N. Neethu, M.R. Amrita Vishwa Vidyapeetham Amrita School of Computing Amrita School of Computing Department of Computer Science and Engineering Coimbatore India Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350335095

A physical document that can be utilized to validate a person's identity is known as ID card, usually comes in standard size same as that of credit cards (3.370 inch height and 2.125 inch width).The machine readable identification information within ID card is the primary means of controlling individual access to everything starting from employee areas to special lounges. Transparency level of identification information within the document (ID card) varies from organization to organization. Some uses visible credentials like Barcodes and QR codes while others use RFIDs. Impersonation and the MFA- SYSTEM represents two distinct phases of the project. Former gives a clear-cut idea on how an impersonation attack is carried out by using methods in image processing, computer vision for extracting the visible features and decoding the information from the images or videos recorded using smartphones or surveillance cameras. SURF and SIFT are initially extracted, FLANN for feature mapping and Homography estimation using RANSAC algorithms. Latter one is making a multifactor authentication system that can improve the security of the ID card credentials. The proposed system in phase 1 can is able to successfully locate the position of barcodes and QR codes within the image and successfully decode information within the QR code, phase two helps to solve some of the challenges in authentication like shoulder surfing, securing data in MongoDB, accuracy during storing and retrieval from database etc © 2023 IEEE.

关键词： Authentication

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Leveraging Industry 4.0 in Education for Remote Implementation in a Team-Based computer engineering Capstone Project 54

Leveraging Industry 4.0 in Education for Remote Implementati...

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54th IEEE Frontiers in Education Conference, FIE 2024

作者： Sasidhar, Sangit Vachery, Jithin National University of Singapore Department of Electrical and Computer Engineering Singapore School of Computing National University of Singapore Singapore

ISBN: (纸本)9798350351507

This Innovative Practice Category Full Paper presents the remote implementation of an embedded systems capstone project for computer engineering students. A capstone project is a feature of most undergraduate programs in computer engineering. Such a project is usually meant to expose students to the development of a large system from conceptualization to its final implementation, involving substantial design and development of hardware and software components. In our university, students were given the opportunity to work on a cutting-edge problem focused on healthcare - 'Designing a wearable device that automatically detects human activities'. It is an area where a large amount of research is ongoing, and hundreds of scientific papers are published every year. In addition, students had to evaluate and adopt techniques from existing literature and adapt them to meet the problem requirements. Students equipped themselves with state-of-the-art hardware, Bio-signal processing, machine learning, power optimization and secure communications to design the wearable. Thus, the project reinforced their knowledge of fundamentals, while exposing them to a problem with no obvious solution. Through the capstone project, students are able to better appreciate the relevance of the various components in the computer engineering curriculum to large-scale computer engineering projects. Students are organized into teams of six to execute the project. The COVID-19 pandemic resulted in the university migrating the teaching online for all courses. This was particularly challenging to implement for the capstone project as one of the key requirements is for the members to work together and subsystems to interact with each other. A course refresh resulted in a credits update and this provided a unique opportunity for the teaching team to re-design the project with core Industry 4.0 technologies such as hardware acceleration, remote processing, networking, remote analytics, and secure protoc

关键词： Students

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computer vision in smart agriculture and precision farming:Techniques and applications

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Artificial Intelligence in Agriculture 2024年第3期13卷 64-83页

作者： Sumaira Ghazal Arslan Munir Waqar S.Qureshi Department of Computer Science Kansas State University66506 ManhattanKSUSA Department of Electrical Engineering and Computer Science Florida Atlantic University33431 Boca RatonFLUSA School of Computer Science University of GalwayH91 TK33 GalwayIreland

The transformation of age-old farming practices through the integration of digitization and automation has sparked a revolution in agriculture that is driven by cutting-edge computer vision and artificial intelligence(AI)*** transformation not only promises increased productivity and economic growth,but also has the potential to address important global issues such as food security and *** survey paper aims to provide a holistic understanding of the integration of vision-based intelligent systems in various aspects of precision *** providing a detailed discussion on key areas of digital life cycle of crops,this survey contributes to a deeper understanding of the complexities associated with the implementation of vision-guided intelligent systems in challenging agricultural *** focus of this survey is to explore widely used imaging and image analysis techniques being utilized for precision farming *** paper first discusses various salient crop metrics used in digital *** this paper illustrates the usage of imaging and computer vision techniques in various phases of digital life cycle of crops in precision agriculture,such as image acquisition,image stitching and photogrammetry,image analysis,decision making,treatment,and *** establishing a thorough understanding of related terms and techniques involved in the implementation of vision-based intelligent systems for precision agriculture,the survey concludes by outlining the challenges associated with implementing generalized computer vision models for real-time deployment of fully autonomous farms.

关键词： Digital agriculture computer vision Smart agriculture Image analysis Vision-guided intelligent systems

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Optimisation and Evaluation of Breadth First Search with oneAPI/SYCL on Intel FPGAs: From Describing Algorithms to Describing Architectures 24

Optimisation and Evaluation of Breadth First Search with one...

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12th International Workshop on OpenCL and SYCL, IWOCL 2024

作者： Olgu, Kaan Kenter, Tobias Nunez-Yanez, Jose McIntosh-Smith, Simon School of Electrical & Electronics Engineering University of Bristol United Kingdom Paderborn Center for Parallel Computing Department of Computer Science Paderborn University Germany Dept. Electrical Engineering Linkoping University Sweden

ISBN: (纸本)9798400717901

FPGAs are often used in scientific fields to process graph algorithms due to their energy efficiency, reconfigurability, and fine-grained parallelism. However, these algorithms face challenges in memory access patterns, scalability, and programmability. The SYCL2020 implementation in the Intel oneAPI toolchain supports FPGA targets alongside SYCL2020 features like modern C++ with a single-source offloading to improve programmability. This study analysed the Breadth-First Search algorithm on Stratix 10 FPGA with the Intel oneAPI toolchain. The implementation was done in two phases. At first, we applied the typical optimisations proposed in the official guidelines alongside an automatic cache to achieve proper pipelining and improve random memory accesses performance. However, limitations occurred with fine-grained parallelism, and it was competitive only to some related work that utilised hardware-description languages or established high-level synthesis tools. For the second phase, we added bit-level representations of data in memory, banking in on-chip memory, and fine-grained control over parallel data streams. The second implementation was generally superior or on par with all compared designs, outperforming other works in 10 out of 15 tested datasets, including various synthetic RMAT and real-world datasets, with a peak performance of 1021 MTEPS. © 2024 Owner/Author.

关键词： Field programmable gate arrays (FPGA)

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Optimizing land mine detection across diverse mining environments: A hyperspectral data approach with regression models

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International Journal of Intelligent Networks 2024年第1期5卷 351-363页

作者： Anand, R. Andrew, J. Makki, Ihab Department of Electrical and Electronics Engineering Amrita School of Engineering Amrita Vishwa Vidyapeetham Coimbatore India Department of Computer Science and Engineering Manipal Institute of Technology Manipal Academy of Higher Education Manipal Karnataka 576104 India Department of Nouvelle Aquitaine France

The detection of landmines, namely anti-tank mines, explosive devices, and unexploded ordnance, is a formidable obstacle for the global community. The visible consequences of unobserved explosives in communities affected by war are characterized by significant devastation and human suffering. In order to effectively tackle this matter, it is imperative to use proactive strategies that focus on the identification and mitigation of these perilous substances prior to their potential infliction of harm. Nevertheless, the majority of current solutions exhibit significant deficiencies, such as exorbitant expenses, inefficiencies, and apprehensions over accuracy. These drawbacks are further compounded by the inherent trade-offs that exist between these elements, where improvements in one area often come at the expense of another. Contrarily, recent breakthroughs in the areas of deep learning, unmanned aerial vehicles, and sensor technologies are being recognized as potentially transformative elements in the domain of landmine identification and removal. This paper presents a thorough examination of recent scholarly investigations that integrate computerized technology in the field of landmine detection. To the extent of our current understanding, there has been no prior investigation that has thoroughly examined this particular domain. The main aim of this study is to investigate the incorporation of machine learning based regression methods in the field of landmine detection. The study specifically emphasizes the identification and resolution of existing issues that hinder the development of efficient automated solutions, hence enhancing performance optimization. The Sum of Sine Curve Fit Regression Model is proposed and proved a powerful and adaptable tool for extracting relevant information from this hyperspectral images. © 2024 The Authors

关键词： Unmanned aerial vehicles (UAV)

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AN 8-WAY RF FEED NETWORK FOR 5.3 GHZ C-BAND SAR: DESIGN, SIMULATION, AND ANALYSIS USING STANDARD METHODS

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Telecommunications and Radio engineering (English translation of Elektrosvyaz and Radiotekhnika) 2025年第5期84卷 21-43页

作者： Kothapudi, Venkata Kishore Thokala, Niharika Vallabhaneni, Ramyasri Sanaka, Poojitha Kumar, Vijay Pappula, Lakshman Gottapu, Srinivasa Kiran Bhattacharyya, Arka Guntur District Andhra Pradesh Vadlamudi522213 India Department of Communication Engineering School of Electronics Engineering Vellore Institute of Technology Tamil Nadu Vellore632014 India Department of Electrical Electronics and Communication Engineering GITAM School of Technology GITAM University Andhra Pradesh Visakhapatnam530045 India School of Science and Engineering Engineering and Computer Science Department Benedict College 1600 Harden St ColumbiaSC29204 United States

This study proposes the design and analysis of an eight-way power divider for unequal division at 5.3 GHz for C-band frequency. Many transmission line pieces make up the current power divider. These transmission lines were printed on a substrate material (RT/duroid 5880, 1.6 mm thick, permittivity 2.2, and loss tangent 0.0002). The eight-way power divider’s unequal amplitudes are constructed using Taylor’s 1-parameter method to achieve the side projection levels –17 dB down as from the main beam. The proposed power divider consists of using two transmission lines with a 3/4 frequency connected to branched microstriplines, providing excellent isolation and matching between the power divider’s two output ports. This power divider is composed of three stages. A 1:2 power divider is used in the first stage, a 1:4 power divider is used in the second stage, and a 1:8 power divider is used in the third stage. According to the simulation results, 1:2 delivers half of the input power, 1:4 produces one-fourth of the power, and 1:8 delivers one-eighth of the input power. All of these assessments were done with the advanced design system (ADS) simulation program. According to both simulated and measured data, the return loss exceeds –35 dB, and the isolation across the output terminals exceeds –40 dB. The proposed circuit operates effectively at C-band frequencies in terms of return loss and isolation (4 GHz and 8 GHz). © 2025 by Begell House, Inc.

关键词： Integrated circuit design

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