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2024 Asian Conference on Intelligent Technologies, ACOIT 2024

作者： Bhargava, Neeraj Rathore, Pramod Singh Goswami, Ankur Chauhan, Kapil Panda, Sankarsan School of Engineering & System Sciences MDS University Ajmer India Department of Computer and Communication Engineering Manipal University Jaipur India Department of Computer Applications Govt. Engineering College Bikaner India Aryabhattt College of Engieering and research Centre India MDS University Ajmer India

ISBN: (纸本)9798350374933

This study presents a comprehensive methodology for fingerprint analysis, employing a sequence of advanced image processing techniques including preprocessing, skeletonization, and minutiae extraction, leading to the generation of a digital bit-string representation. By enhancing image quality through histogram equalization and adaptive thresholding, and by refining fingerprint ridge patterns to single pixel widths via skeletonization, the proposed techniques facilitate precise minutiae extraction. These steps significantly improve the accuracy and reliability of biometric identification systems, as demonstrated by the dense and accurate distribution of minutiae points. The resultant digital encoding supports efficient data storage and rapid matching processes, essential for robust biometric verification. This research underscores the potential of these methodologies to enhance the performance of fingerprint-based security systems and lays the groundwork for future integrations with machine learning for automated and scalable biometric solutions. © 2024 IEEE.

关键词： Musculoskeletal system

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Optimized Evaluation of Mobile Base Station by Modern Topological Invariants

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computers, Materials & Continua 2023年第1期74卷 363-378页

作者： Khalid Hamid Muhammad Waseem Iqbal Muhammad Usman Ashraf Ahmed Mohammed Alghamdi Adel A.Bahaddad Khalid Ali Almarhabi Department of Computer Science Superior UniversityLahore54000Pakistan Department of Software Engineering Superior UniversityLahore54000Pakistan Department of Computer Science GC Women UniversitySialkot53310Pakistan Department of Software Engineering College of Computer Science and EngineeringUniversity of Jeddah21493Saudi Arabia Department of Information System Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Computer Science College of Computing in Al-QunfudahUmm Al-Qura UniversityMakkah24381Saudi Arabia

Due to a tremendous increase in mobile traffic,mobile operators have started to restructure their networks to offload their *** directions will lead to fundamental changes in the design of future Fifthgeneration(5G)cellular *** the formal reason,the study solves the physical network of the mobile base station for the prediction of the best characteristics to develop an enhanced network with the help of graph *** number that can be uniquely calculated by a graph is known as a graph *** the last two decades,innumerable numerical graph invariants have been portrayed and used for correlation *** any case,no efficient assessment has been embraced to choose,how much these invariants are connected with a network *** paper will talk about two unique variations of the hexagonal graph with great capability of forecasting in the field of optimized mobile base station topology in setting with physical *** K-banhatti sombor invariants(KBSO)and Contrharmonic-quadratic invariants(CQIs)are newly introduced and have various expectation characteristics for various variations of hexagonal graphs or *** the hexagonal networks are used in mobile base stations in layered,forms called *** review settled the topology of a hexagon of two distinct sorts with two invariants KBSO and CQIs and their reduced *** deduced outcomes can be utilized for the modeling of mobile cellular networks,multiprocessors interconnections,microchips,chemical compound synthesis and memory interconnection *** results find sharp upper bounds and lower bounds of the honeycomb network to utilize the Mobile base station network(MBSN)for the high load of traffic and minimal traffic also.

关键词： Hexagonal networks k-banhatti sombor invariants maple network graph artificial intelligence contraharmonic-quadratic invariants honeycomb

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IoT and AI-Driven Conceptual Model of Museum Ecosystem

IoT and AI-Driven Conceptual Model of Museum Ecosystem

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2023 International Conference on Electrical engineering and Informatics, ICEEI 2023

作者： Puspasari, Shinta Siradjuddin, Indah Agustien Rachmansyah Fahtu Rahman, M. Ade Haversyalapa, Ditho Universitas Indo Global Mandiri Faculty of Computer Science Department of Informatics Palembang Indonesia Universitas Trunojoyo Faculty of Engineering Department of Informatics Madura Indonesia Universitas Indo Global Mandiri Faculty of Computer Science Department of Computer System Palembang Indonesia

ISBN: (纸本)9798350381290

Museum ecosystems need digital transformation to provide value added to museums. This study discusses the digital museum ecosystem conceptual model based on artificial intelligence (AI) technology and the Internet of Things (IoT). The new conceptual model integrates three elements of the museum ecosystem, people, collections & infrastructure, and museum services collaborating and communicating to achieve outputs in supporting the achievement of museum organizational goals through IoT and AI-based digital technology. The conceptual model describes various forms of implementing IoT and AI in the museum ecosystem for museum management along with the value added or results generated for museum management. The development of this ecosystem conceptual model into a physical museum has an impact on optimizing museum performance by providing value added in the form of convenience and prosperity for users and various museum stakeholders, especially in Society 5.0. © 2023 IEEE.

关键词： Internet of things

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Fingerprint Image Enhancement: Multi-Filter Approaches and Stage Timing Evaluation 4

Fingerprint Image Enhancement: Multi-Filter Approaches and S...

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4th International Conference on Sustainable Expert systems, ICSES 2024

作者： Bhargava, Neeraj Rathore, Pramod Singh Goswami, Ankur Chauhan, Kapil Choudhary, Suresh School of Engineering & System Sciences Mds University Ajmer India Manipal University Department of Computer and Communication Engineering Jaipur India Govt. Engineering College Department of Computer Applications Bikaner India Aryabhattt College of Engieering and research Centre India Mds University Ajmer India

ISBN: (纸本)9798331540364

The performance of fingerprint recognition and verification systems heavily depends on the quality of the input fingerprint images. This paper introduces a multi-filter image enhancement strategy tailored for enhancing low-quality fingerprint images. The objective is to enhance the visibility and definition of ridge patterns, tackling problems stemming from irregular skin contacts and contaminated sensor surfaces. Author incorporates a new normalization technique that adjusts for finger flexibility, improving the correction of anomalies and boosting overall image quality, especially in detecting fine details. Experimental evidence shows that technique surpasses previous methods in effectiveness. © 2024 IEEE.

关键词： Gabor filters

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Diagnosis of COVID-19 on Chest X-ray (CXR) Images Using CNN with Transfer Learning and Integrated Stacking Ensemble Learning 1

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4th International conference on Innovative Manufacturing, Mechatronics and Materials Forum, iM3F2023

作者： Low, Wai Sing Chow, Li Sze Solihin, Mahmud Iwan Handayani, Dini Oktarina Dwi Department of Electrical and Electronic Engineering Faculty of Engineering Technology and Built Environment UCSI University Cheras Malaysia Department of Mechanical Engineering Faculty of Engineering Technology and Built Environment UCSI University Cheras Malaysia Computer Science Department Faculty of Information Communication and Technology International Islamic University Malaysia Kuala Lumpur Malaysia

ISBN: (数字)9789819988198

ISBN: (纸本)9789819988181

COVID-19 caused a pandemic outbreak, resulting in many deaths and severe economic damage since 2019. Hence, the diagnosis of COVID-19 has become one of the major fields of research. Although RT-PCR has excellent reliability and precision, it is time-consuming and laborious. Therefore, the chest X-ray was used as an alternative and reliable diagnostic tool for COVID-19. However, it requires a radiologist to analyze the X-ray images, which is limited by the availability of experts and time. Henceforth, many researchers deployed automated computer-aided diagnosis with deep learning neural networks to speed up the diagnosis of COVID-19 with high accuracy and reproducibility. This study applied six state-of-art convolutional neural networks (DenseNet201, MobileNetV2, ResNet101V2, VGG16, InceptionNetV3, and Xception) with transfer learning. An integrated stacking ensemble method was used to concatenate DenseNet201, MobileNetV2, VGG16, and Xception to produce a robust and accurate diagnostic model for COVID-19. The proposed ensembled CNN model in this study produced a test accuracy of 0.9725, sensitivity of 0.9749, and F1-score of 0.9724. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： COVID-19

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Virtual Twin Analytics: Advancing IoT Security through AI-Powered Software Informatics 1

Virtual Twin Analytics: Advancing IoT Security through AI-Po...

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1st International Conference on Software, systems and Information Technology, SSITCON 2024

作者： Nanthini, S. Jain, Upma Arora, Renuka Bhatia, Rishu Sutaria, Kruti Patil, Harshal Saveetha School of Engineering Department of Computer Science and Engineering Chennai India Graphic Era Deemed to Be University Department of Computer Science and Engineering Dehradun India Jagannath University Department of Computer Science and Engineering Haryana India Ganga Institute of Technology and Management Department of Electronics and Communication Engineering Haryana India Parul Institute of Engineering and Technology Faculty of Engineering and Technology Gujarat India School of Computer Science and Engineering Uttar Pradesh India

ISBN: (纸本)9798350352931

The Internet has grown in importance and impact over the years, causing people to become more reliant on it. The Internet has evolved into a major vector for cybercrime because to its ever-increasing user base. Over the last decade, the number of these computing systems - including desktops, laptops, smartphones, and the Internet of Things (IoT) - has skyrocketed. Among them, cell phones are practically integral to modern life. The popularity of web-based assaults has skyrocketed with the exponential growth in the number of individuals using the Internet. These web-based assaults are increasingly being combatted by security corporations. Unfortunately, new forms of these assaults are appearing all the time, making it hard for older security measures to stay up. Artificial intelligence (AI) is a source of optimism in the current cybersecurity landscape, offering a potential solution to the ever-changing digital dangers. The fast development of AI over the last decade has given rise to this optimism, because it is now impacting the expansion of every industry. With AI bringing so many advantages in every field, online security is one sector that just cannot afford to ignore it. This planned effort's work represents an advance in that direction. Critical online security issues have been the focus of this proposed work's study, which aims to address these issues using AI. Web security issues for desktop and mobile devices have been addressed in the proposed work. The planned work's contributions to online security are as follows: The 'MalCrawler' web crawler is a targeted tool for finding and exploring the web. This crawler makes it easy to gather websites, particularly ones that are harmful. It does a better job of collecting dangerous websites than a typical crawler. Additionally, it is built to circumvent the evasion strategies used by rogue websites. The crawler's ability to gather webpages - particularly dangerous ones - in order to provide datasets for ML-based an

关键词： Web crawler

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Leveraging EfficientNetB3 in a Deep Learning Framework for High-Accuracy MRI Tumor Classification

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computers, Materials & Continua 2024年第10期81卷 867-883页

作者： Mahesh Thyluru Ramakrishna Kuppusamy Pothanaicker Padma Selvaraj Surbhi Bhatia Khan Vinoth Kumar Venkatesan Saeed Alzahrani Mohammad Alojail Department of Computer Science&Engineering Faculty of Engineering and TechnologyJAIN(Deemed-to-be University)Bengaluru562112India School of Computer Science and Engineering VIT-AP UniversityAmaravati522241India Department of Computer Science&Technology Madanapalle Institute of Technology&ScienceMadanapalle517325India School of Science Engineering and EnvironmentUniversity of SalfordManchesterM54WTUK School of Computer Science Engineering&Information Systems(SCORE) Vellore Institute of Technology(VIT)Vellore632014India Management Information System Department College of Business AdministrationKing Saud UniversityRiyadh11531Saudi Arabia Adjunct Research Faculty Centre for Research Impact&OutcomeChitkara UniversityRajpuraPunjab140401India

Brain tumor is a global issue due to which several people suffer,and its early diagnosis can help in the treatment in a more efficient *** different types of brain tumors,including gliomas,meningiomas,pituitary tumors,as well as confirming the absence of tumors,poses a significant challenge using MRI *** approaches predominantly rely on traditional machine learning and basic deep learning methods for image *** methods often rely on manual feature extraction and basic convolutional neural networks(CNNs).The limitations include inadequate accuracy,poor generalization of new data,and limited ability to manage the high variability in MRI *** the EfficientNetB3 architecture,this study presents a groundbreaking approach in the computational engineering domain,enhancing MRI-based brain tumor *** approach highlights a major advancement in employing sophisticated machine learning techniques within computer Science and engineering,showcasing a highly accurate framework with significant potential for healthcare *** model achieves an outstanding 99%accuracy,exhibiting balanced precision,recall,and F1-scores across all tumor types,as detailed in the classification *** successful implementation demonstrates the model’s potential as an essential tool for diagnosing and classifying brain tumors,marking a notable improvement over current *** integration of such advanced computational techniques in medical diagnostics can significantly enhance accuracy and efficiency,paving the way for wider *** research highlights the revolutionary impact of deep learning technologies in improving diagnostic processes and patient outcomes in neuro-oncology.

关键词： Deep learning MRI brain tumor cassification EfficientNetB3 computational engineering healthcare technology artificial intelligence in medical imaging tumor segmentation neuro-oncology

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Spatial Attention Integrated EfficientNet Architecture for Breast Cancer Classification with Explainable AI

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computers, Materials & Continua 2024年第9期80卷 5029-5045页

作者： Sannasi Chakravarthy Bharanidharan Nagarajan Surbhi Bhatia Khan Vinoth Kumar Venkatesan Mahesh Thyluru Ramakrishna Ahlam AlMusharraf Khursheed Aurungzeb Department of Electronics and Communication Engineering Bannari Amman Institute of TechnologySathyamangalam638402India School of Computer Science Engineering and Information Systems(SCORE) Vellore Institute of TechnologyVellore632014India School of Science Engineering and EnvironmentUniversity of SalfordManchesterM54WTUK Department of Computer Science&Engineering Faculty of Engineering and TechnologyJAIN(Deemed-to-be University)Bengaluru562112India Department of Management College of Business AdministrationPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityP.O.Box 51178Riyadh11543Saudi Arabia Adjunct Research Faculty Centre for Research Impact&OutcomeChitkara UniversityRajpura140401India

Breast cancer is a type of cancer responsible for higher mortality rates among *** cruelty of breast cancer always requires a promising approach for its earlier *** light of this,the proposed research leverages the representation ability of pretrained EfficientNet-B0 model and the classification ability of the XGBoost model for the binary classification of breast *** addition,the above transfer learning model is modified in such a way that it will focus more on tumor cells in the input ***,the work proposed an EfficientNet-B0 having a Spatial Attention Layer with XGBoost(ESA-XGBNet)for binary classification of *** this,the work is trained,tested,and validated using original and augmented mammogram images of three public datasets namely CBIS-DDSM,INbreast,and MIAS *** accuracy of 97.585%(CBISDDSM),98.255%(INbreast),and 98.91%(MIAS)is obtained using the proposed ESA-XGBNet architecture as compared with the existing ***,the decision-making of the proposed ESA-XGBNet architecture is visualized and validated using the Attention Guided GradCAM-based Explainable AI technique.

关键词： EfficientNet mammograms breast cancer Explainable AI deep-learning transfer learning

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Behaviour-diverse automatic penetration testing:a coverage-based deep reinforcement learning approach

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Frontiers of computer Science 2025年第3期19卷 15-24页

作者： Yizhou YANG Longde CHEN Sha LIU Lanning WANG Haohuan FU Xin LIU Zuoning CHEN Zhongguancun Laboratory Beijing 100081China Zhejiang Lab Hangzhou 311121China National Research Centre of Parallel Computer Engineering and Technology Wuxi 214000China Faculty of Geographical Science Beijing Normal UniversityBeijing 100875China Department of Earth System Science Tsinghua UniversityBeijing 100084China

Reinforcement Learning(RL)is gaining importance in automating penetration testing as it reduces human effort and increases ***,given the rapidly expanding scale of modern network infrastructure,the limited testing scale and monotonous strategies of existing RLbased automated penetration testing methods make them less effective in practical *** this paper,we present CLAP(Coverage-Based Reinforcement Learning to Automate Penetration Testing),an RL penetration testing agent that provides comprehensive network security assessments with diverse adversary testing behaviours on a massive *** employs a novel neural network,namely the coverage mechanism,to address the enormous and growing action spaces in large *** also utilizes a Chebyshev decomposition critic to identify various adversary strategies and strike a balance between *** results across various scenarios demonstrate that CLAP outperforms state-of-the-art methods,by further reducing attack operations by nearly 35%.CLAP also provides enhanced training efficiency and stability and can effectively perform pen-testing over large-scale networks with up to 500 ***,the proposed agent is also able to discover pareto-dominant strategies that are both diverse and effective in achieving multiple objectives.

关键词： network security penetration testing reinforcement learning artificial intelligence

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Wide-Angle Scanning Antennas for Millimeter-Wave 5G Applications

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engineering 2022年第4期8卷 60-71页

作者： Raj Mittra Abdelkhalek Nasri Ravi Kumar Arya Electrical and Computer Engineering University of Central FloridaOrlandoFL 32816USA Electrical and Computer Engineering Department Faculty of EngineeringKing Abdulaziz UniversityJeddah 21589Saudi Arabia Department of Electronics and Communication Engineering National Institute of Technology DelhiDelhi 110040India

The fifth generation(5G)network communication systems operate in the millimeter waves and are expected to provide a much higher data rate in the multi-gigabit range,which is impossible to achieve using current wireless services,including the sub-6 GHz *** this work,we briefly review several existing designs of millimeter-wave phased arrays for 5G applications,beginning with the low-profile antenna array designs that either are fixed beam or scan the beam only in one *** then move on to array systems that offer two-dimensional(2D)scan capability,which is highly desirable for a majority of 5G ***,in the main body of the paper,we discuss two different strategies for designing scanning arrays,both of which circumvent the use of conventional phase shifters to achieve beam *** note that it is highly desirable to search for alternatives to conventional phase shifters in the millimeter-wave range because legacy phase shifters are both lossy and costly;furthermore,alternatives such as active phase shifters,which include radio frequency amplifiers,are both expensive and *** this backdrop,we propose two different antenna systems with potential for the desired 2D scan performance in the millimeter-wave *** first of these is a Luneburg lens,which is excited either by a 2D waveguide array or by a microstrip patch antenna array to realize 2D scan ***,for second design,we turn to phased-array designs in which the conventional phase shifter is replaced by switchable PIN diodes or varactor diodes,inserted between radiating slots in a waveguide to provide the desired phase shifts for ***,we discuss several approaches to enhance the gain of the array by modifying the conventional array *** describe novel techniques for realizing both one-dimensional(1D)and 2D scans by using a reconfigurable metasurface type of panels.

关键词： Luneburg lens Scanning arrays Millimeter-wave antennas Switchable phase shifters Reconfigurable metasurfaces

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