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UNet Based onMulti-Object Segmentation and Convolution Neural Network for Object Recognition

computers, Materials & Continua 2024年第7期80卷 1563-1580页

作者： Nouf Abdullah Almujally Bisma Riaz Chughtai Naif Al Mudawi Abdulwahab Alazeb Asaad Algarni Hamdan A.Alzahrani Jeongmin Park Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Science Air UniversityIslamabad44000Pakistan Department of Computer Science College of Computer Science and Information SystemNajran UniversityNajran55461Saudi Arabia Department of Computer Sciences Faculty of Computing and Information TechnologyNorthern Border UniversityRafha91911Saudi Arabia Information Technology Department College of Computing and InformaticsSaudi Electronic UniversityRiyadh13316Saudi Arabia Department of Computer Engineering Korea Polytechnic UniversitySiheung-siGyeonggi-do237Republic of Korea

The recent advancements in vision technology have had a significant impact on our ability to identify multiple objects and understand complex *** technologies,such as augmented reality-driven scene integration,robotic navigation,autonomous driving,and guided tour systems,heavily rely on this type of scene *** paper presents a novel segmentation approach based on the UNet network model,aimed at recognizing multiple objects within an *** methodology begins with the acquisition and preprocessing of the image,followed by segmentation using the fine-tuned UNet ***,we use an annotation tool to accurately label the segmented *** labeling,significant features are extracted from these segmented objects,encompassing KAZE(Accelerated Segmentation and Extraction)features,energy-based edge detection,frequency-based,and blob *** the classification stage,a convolution neural network(CNN)is *** comprehensive methodology demonstrates a robust framework for achieving accurate and efficient recognition of multiple objects in *** experimental results,which include complex object datasets like MSRC-v2 and PASCAL-VOC12,have been *** analyzing the experimental results,it was found that the PASCAL-VOC12 dataset achieved an accuracy rate of 95%,while the MSRC-v2 dataset achieved an accuracy of 89%.The evaluation performed on these diverse datasets highlights a notably impressive level of performance.

关键词： UNet segmentation blob fourier transform convolution neural network

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Machine learning assisted snort and zeek in detecting DDoS attacks in software-defined networking

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International Journal of Information Technology (Singapore) 2024年第3期16卷 1627-1643页

作者： AbdulRaheem, Muyideen Oladipo, Idowu Dauda Imoize, Agbotiname Lucky Awotunde, Joseph Bamidele Lee, Cheng-Chi Balogun, Ghaniyyat Bolanle Adeoti, Joshua Oluwatobi Department of Computer Science Faculty of Information and Communication Sciences University of Ilorin Ilorin 240003 Nigeria Department of Electrical and Electronics Engineering Faculty of Engineering University of Lagos Akoka Lagos 100213 Nigeria Department of Electrical Engineering and Information Technology Institute of Digital Communication Ruhr University Bochum 44801 Germany Research and Development Center for Physical Education Health and Information Technology Department of Library and Information Science Fu Jen Catholic University New Taipei City 24205 Taiwan Department of Computer Science and Information Engineering Asia University Taichung City 41354 Taiwan

A new network architecture called the Software-Defined Network (SDN) gives next-generation networks a more flexible and efficiently controlled network architecture. Using the programmable central controller design, network supervisors may easily supervise and manage the entire infrastructure. However, due to its centralized structure, SDN has been a target of various attack vectors. The most successful attack method against the SDN among these has been Distributed Denial of Service (DDoS). Therefore, this study proposes a snort and Zeek enabled with machine learning (ML) based model to classify the benign traffic from DDoS attack traffic. This study main contribution is the discovery of new features for DDoS attack detection, which made it difficult to distinguish authorized traffic from attack traffic when spread across so many points of origin. Using the ML-based enabled RYU controller with SNORT and ZEEK created fewer false positives and a smaller variety of true positives per attack than the existing methods. The processing time of ML-based enabled with SNORT and ZEEK on the real-time testbed is better contrasted to the existing methods. Using the open resource technologies offered a far better understanding of cyber safety and its benefits from the readily available programs to construct a solid network keeping an eye on the traffic. © 2023, The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of computer Applications and Management.

关键词： Cybersecurity Distributed denial of service Intrusion detection systems Snort Software-defined networks Virtual machine Zeek

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Human Action Recognition Using Difference of Gaussian and Difference of Wavelet

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Big Data Mining and Analytics 2023年第3期6卷 336-346页

作者： Gopampallikar Vinoda Reddy Kongara Deepika Lakshmanan Malliga Duraivelu Hemanand Chinnadurai Senthilkumar Subburayalu Gopalakrishnan Yousef Farhaoui Department of Computer Science and Engineering(AI&ML) CMR Technical CampusHyderabad 501401India. Department of Information Technology Kakatiya Institute of Technology and ScienceWarangal 506015India. Department of Electronics and Communication Engineering Malla Reddy Engineering College for Women(Autonomous)Hyderabad 500100India. Department of Computer Science and Engineering S.A.Engineering College(Autonomous)Thiruverkadu 600077India. Department of Electronics and Communication Engineering Saveetha Institute of Medical and Technical Sciences(SIMATS)Chennai 602105India. Department of Electronics and Communication Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and TechnologyChennai 600062India. IDMS Team STI LaboratoryFaculty of Sciences and TechniquesMoulay Ismail University of MeknèsErrachidia 25003Morocco.

Human Action Recognition(HAR)attempts to recognize the human action from images and *** major challenge in HAR is the design of an action descriptor that makes the HAR system robust for different environments.A novel action descriptor is proposed in this study,based on two independent spatial and spectral *** proposed descriptor uses a Difference of Gaussian(DoG)filter to extract scale-invariant features and a Difference of Wavelet(DoW)filter to extract spectral *** create a composite feature vector for a particular test action picture,the Discriminant of Guassian(DoG)and Difference of Wavelet(DoW)features are *** Discriminant Analysis(LDA),a widely used dimensionality reduction technique,is also used to eliminate duplicate ***,a closest neighbor method is used to classify the *** and UCF 11 datasets were used to run extensive simulations of the suggested strategy,and the accuracy assessed after the simulations were run on Weizmann datasets for five-fold cross validation is shown to perform *** average accuracy of DoG+DoW is observed as 83.6635%while the average accuracy of Discrinanat of Guassian(DoG)and Difference of Wavelet(DoW)is observed as 80.2312%and 77.4215%,*** average accuracy measured after the simulation of proposed methods over UCF 11 action dataset for five-fold cross validation DoG+DoW is observed as 62.5231%while the average accuracy of Difference of Guassian(DoG)and Difference of Wavelet(DoW)is observed as 60.3214%and 58.1247%,*** the above accuracy observations,the accuracy of Weizmann is high compared to the accuracy of UCF 11,hence verifying the effectiveness in the improvisation of recognition accuracy.

关键词： human action recognition difference of Gaussian difference of wavelet linear discriminant analysis Weizmann UCF 11 accuracy

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Super Six - Axis Cord Influenced Linear Laser for Personal Flower Management in Cultivation with Live Control systems is Connected to The Robot Manipulator 11

Super Six - Axis Cord Influenced Linear Laser for Personal F...

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11th International Conference on system Modeling and Advancement in Research Trends, SMART 2022

作者： Narasimhayya, B.E. Vinay Kumar, S.B. Department of Computer Science and Engineering Bangalore India Department of Electronics and Communication Engineering Faculty of Engineering and Technology Bangalore India

ISBN: (纸本)9781665487320

In today's agribusiness, using robotics, machine learning, and also the web of things has become standard practise. The agricultural business is being forced to embrace such new methods in order to address issues like massive unemployment, rising food consumption with declining quality farmland, efficiency per square land, and deteriorating food quality due to global warming. As a result of the aforementioned difficulties and the world's increasing population, research and innovation in agricultural technology have become more crucial nowadays. In this document, we suggest an AI-powered wire automaton with six levels of autonomy and an omega automaton with degrees of opportunity that can help with agricultural tasks like sorting, pest management, picking strawberries and flowers, fertiliser application, and tracking of plant development, NPK tiers, and soil moisture. The robotic is called Farmbot. The Based criteria may communicate crop information to distant servers where it can be processed and retrieved to get further insight into soil quality. It can also be managed and watched remotely by a home computer or via a mobile internet software. The Farmbot may be programmed to do routine tasks including continuous monitoring, collecting fruit, cleaning, applying fertiliser, and gathering information about each plant. The Farmbot has direct exposure to just about any area of the field thanks to its placement over a moveable four wheels bot. This Farmbot's goal is mainly on providing individualised plant maintenance and aiding producers in raising output. As a design illustration of a functioning version for the imagined Farmbot, a 3D depiction is shown. The simulation's outcomes are shown. © 2022 IEEE.

关键词： Remote control

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Multiband Slotted Crescent-shaped Patch Antenna for K-band Satellite and mmWave communications

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IEIE Transactions on Smart Processing and Computing 2022年第3期11卷 213-221页

作者： Hossain, Md Najmul Paul, Liton Chandra Rahim, Md Abdur Shin, Jungpil Department of Electrical Electronic and Communication Engineering Faculty of Engineering and Technology Pabna University of Science and Technology Pabna6600 Bangladesh Department of Computer Science and Engineering Faculty of Engineering and Technology Pabna University of Science and Technology Pabna6600 Bangladesh School of Computer Science and Engineering The University of Aizu Aizuwakamatsu Fukushima965-8580 Japan

In this paper, a multiband miniaturized crescent-shaped patch antenna with circular slots is presented for ultra-wideband applications. The proposed antenna is constructed on a Flame Retardant 4 (FR-4) dielectric substrate. Next-generation wireless communication systems, such as beyond 5G (B5G) and 6G mobile communication systems require a wider bandwidth and higher data rates. The overall size of the proposed antenna is 11.4 mm3 operating in the frequency range of 17.73 GHz to 26.04 GHz and 29.6 GHz to 31.02 GHz for satellite communications and radar systems, respectively, as well as 35.40 GHz to 38.65 GHz for future mmWave B5G mobile applications. These frequency ranges usually fall into the K-band and mmWave frequency spectrums. Simulation results demonstrate that the peak gain of the proposed antenna is 4.68 dB, which is obtained at 25 GHz. © 2022 The Institute of Electronics and Information Engineers.

关键词： Microstrip antennas

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Smart Manure Recommendation using Artificial Intellgience 1

Smart Manure Recommendation using Artificial Intellgience

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1st IEEE International Interdisciplinary Humanitarian Conference for Sustainability, IIHC 2022

作者： Karthik Raghunath, K.M. Kamalraj, R. Faculty of Engineering and Technology Department of Electronics and Communication Engineering Bangalore India Department of Computer Science and IT Bangalore India

ISBN: (纸本)9781665456876

The rapid advancement of wireless sensor networks has prompted the invention of low-cost and compact sensor devices, with the Internet of Things (IoT) enabled as a feasible tool for automation and decision-making in the field of agriculture. This is a significant step forward. The purpose of this study is to investigate the feasibility of developing an expert system for determining the appropriateness of agricultural land by combining sensor networks with artificial intelligence (AI) systems. This proposed system will assist farmers in evaluating agricultural land for cultivation in terms of four decision classes, determining why manure regimes should be adjusted to meet the demands of specific crops and regions, and protect the environment by lowering the amount of pollution caused by unused fertilizer and manure. There are a few methods for the management of soil richness, such as assessing the quality of the soil in terms of its pH level and the amounts of phosphate, nitrogen, and potassium it contains. An ANN model uses climate data to make suggestions and estimates about the amounts of NPK supplements, as well as suggestions about how compost should be treated and when it should be applied.. © 2022 IEEE.

关键词： Internet of things

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Brain Tumor Detection and Segmentation Using RCNN

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computers, Materials & Continua 2022年第6期71卷 5005-5020页

作者： Maham Khan Syed Adnan Shah Tenvir Ali Quratulain Aymen Khan Gyu Sang Choi Department of Computer Science University of Engineering&TechnologyTaxila47080Pakistan Faculty of Computing The Islamia University of BahawalpurBahawalpur63100Pakistan Department of Information and Communication Engineering Yeungnam UniversityGyeongbuk38541Korea

Brain tumors are considered as most fatal *** reduce the risk of death,early identification of the disease is *** of the best available methods to evaluate brain tumors is Magnetic resonance Images(MRI).Brain tumor detection and segmentation are tough as brain tumors may vary in size,shape,and *** makes manual detection of brain tumors by exploring MRI a tedious job for radiologists and doctors’.So an automated brain tumor detection and segmentation is *** work suggests a Region-based Convolution Neural Network(RCNN)approach for automated brain tumor identification and segmentation using MR images,which helps solve the difficulties of brain tumor identification efficiently and *** methodology is based on the accurate and efficient selection of tumorous *** reduces computational complexity and *** have validated the designed experimental setup on a standard dataset,BraTS *** used binary evaluation matrices based on Dice Similarity Coefficient(DSC)and Mean Average Precision(mAP).The segmentation results are compared with state-of-the-art methodologies to demonstrate the effectiveness of the proposed *** suggested approach attained an averageDSC of 0.92 andmAP 0.92 for 10 patients,while on the whole dataset,the scores are DSC 0.89 and mAP *** following results clearly show the performance efficiency of the proposed methodology.

关键词： Brain tumor MRI preprocessing image segmentation brain tumor localization medical ML RCNN BraTS 2020 LGG HGG

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Generation of OAM Waves and Analysis of Mode Purity for 5G Sub-6 GHz Applications

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computers, Materials & Continua 2023年第1期74卷 2239-2259页

作者： Shehab Khan Noor Arif Mawardi Ismail Mohd Najib Mohd Yasin Mohamed Nasrun Osman Nurulazlina Ramli Ali H.Rambe J.Iqbal Advanced Communication Engineering(ACE) Centre of ExcellenceFaculty of Electronic Engineering TechnologyUniversiti Malaysia PerlisKangarPerlisMalaysia Centre for Advanced Electrical&Electronic System(CAEES)Faculty of Engineering Built Environment and Information TechnologySEGi University Petaling JayaSelangorMalaysia Department of Electrical Engineering Universitas Sumatera UtaraMedan20155Indonesia School of Electrical and Electronic Engineering Engineering CampusUniversiti Sains MalaysiaNibong Tebal14300PenangMalaysia Faculty of Engineering&Technology Gomal University29050 kpkPakistan

This article presents the generation of Orbital AngularMomentum(OAM)vortex waves with mode 1 using Uniform Circular Array(UCA)*** different designs,namely,UCA-1(4-element array antenna)and UCA-2(8-element array antenna),were designed and fabricated using FR-4 substrate to generate OAM mode 1 at 3.5 GHz(5G mid-band).The proposed antenna arrays comprised rectangular microstrip patch elements with inset fed *** elements were excited by a carefully designed feeding phase shift network to provide similar output energy at output ports with desired phase shift *** generated OAM waves were confirmed by measuring the null in the bore sight of their 2D radiation patterns,simulated phase distribution and intensity *** measurement results agree well with the simulation ***,a detailed mode purity analysis of the generated OAM waves was carried out considering different *** investigation found that the greater the number of elements,the higher the purity of the generated OAM *** with other previous works,the proposed antenna design of this paper is very simple to design and *** addition,the proposed antennas are compact in design even at lower frequency band with very wide bandwidth to meet the requirements of 5G mid-band applications.

关键词： Orbital angular momentum vortex waves fifth generation wireless communications uniform circular array antenna patch antenna

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Polarization Insensitive Broadband Zero Indexed Nano-Meta Absorber for Optical Region Applications

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computers, Materials & Continua 2022年第4期71卷 993-1009页

作者： Ismail Hossain Md Samsuzzaman Ahasanul Hoque Mohd Hafiz Baharuddin Norsuzlin Binti Mohd Sahar Mohammad Tariqul Islam Space Science Center(ANGKASA) Universiti Kebangsaan Malaysia43600UKM BangiSelangorMalaysia Department of Computer and Communication Engineering Faculty of Computer Science and EngineeringPatuakhali Science and Technology UniversityDumki8602PatuakhaliBangladesh Department of Electrical Electronic and Systems EngineeringFaculty of Engineering and Built EnvironmentUniversiti Kebangsaan Malaysia43600UKM BangiSelangorMalaysia

Broadband response metamaterial absorber(MMA)remains a challenge among researchers.A nanostructured new zero-indexed metamaterial(ZIM)absorber is presented in this study,constructed with a hexagonal shape resonator for optical region *** design consists of a resonator and dielectric layers made with tungsten and quartz(Fused).The proposed absorbent exhibits average absorption of more than 0.8972(89.72%)within the visible wavelength of 450–600 nm and nearly perfect absorption of 0.99(99%)at 461.61 *** on computational analysis,the proposed absorber can be characterized as *** developments of ZIM absorbers have demonstrated plasmonic resonance characteristics and a perfect impedance *** incidence obliquity in typically the range of 0◦–90◦both in TE and TM mode with maximum absorbance is more than 0.8972(∼89.72%),and up to 45◦angular stability is suitable for solar cell applications,like exploiting solar *** proposed structure prototype is designed and simulated by studying microwave technology numerical computer simulation(CST)*** finite integration technique(FIT)based simulator CST and finite element method(FEM)based simulator HFSS also helps validate the numerical data of the proposed ZIM *** proposed MMA design is appropriate for substantial absorption,wide-angle stability,absolute invisible layers,magnetic resonance imaging(MRI),color images,and thermal imaging applications.

关键词： Hexagonal resonator loaded star shaped polarization insensitive compact broadband metamaterial absorber optical region applications

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Insights into the Future: XAI Integration in O-RAN and Space communication systems

Insights into the Future: XAI Integration in O-RAN and Space...

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2024 Multimedia University engineering Conference, MECON 2024

作者： Tahir, H. Ahmed Nabi, Fahad Tariq, Muhammad Ziad Khan, Ahmad Fasih Mahmud, Azwan Multimedia University Faculty of Engineering Cyberjaya Malaysia University of Kiel Department of Electrical Engineering & Information Technology Kiel Germany Department of Computer Science SZABIST University Islamabad Pakistan Roots School System Islamabad Pakistan

ISBN: (纸本)9798331530747

This paper outlines the merging of Explainable Artificial Intelligence (XAI) with Open Radio Access Network (O-RAN) and space communication systems. The usage of AI drives decision-making in critical domains of telecommunications and space explorations requires transparency and interpretability. However, traditional AI models often operate as 'black boxes,' making it difficult to understand their decision-making processes. This lack of explainability poses significant risks, including misinterpretation of signals, undetected anomalies, and erroneous decision-making, which can compromise the integrity of communication systems. Specifically, the challenges include accurately distinguishing between legitimate signals and attacks in anti-jamming scenarios, understanding the behaviour of complex models like LSTM in traffic prediction, and ensuring the reliability of telemetry data despite errors and noise. Integration of XAI techniques within O-RAN architecture and space communication protocols can ensure trust, reliability and safety in AI-enabled systems. This paper investigates the opportunities and challenges of incorporating XAI in O-RAN and space communications. Additionally, it presents the implications of XAI, the need for interpretability in autonomous spacecraft operations, anomaly detection and decision support for mission-critical tasks. By bridging the gap in AI transparency and advanced communication technology, this paper aims to present a detailed analysis of implemented AI and Machine Learning (ML) in the telecommunication domain. It also presents the challenges currently faced in the intersection of XAI and communication. Use cases of XAI in the domain of Open Radio Access Network and Space communications in light of LIME and SHAP techniques were presented to give a hypothetical modelling for future experimentation to provide local and global interpretation and explanations for the currently employed AI/ML models in the respective domains. Overall, this

关键词： Radio access networks

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