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6th International Conference on Power, Energy and Innovations, ICPEI 2024

作者： Phanomupatum, Pratya Phromloungsri, Ravee Tama, Rachata Pongthavornkamol, Tiwat Wongpatsa, Decha Sonasang, Somchat Nakhon Phanom University Faculty Industrial Technology Department of Electronics Nakhon Phanom Thailand Udon Thani Rajabhat University Computer and Communication Engineering Faculty of Technology Udon Thani Thailand National Electronic and Computer Technology Center National Science and Technology Development Agency Location and Automatic Identification System Research Team Communication and Network Research Group Pathumthani Thailand Faculty Industrial Technology Nakhon Phanom Thailand

ISBN: (纸本)9798350356779

The design of a 180° output phase difference Wilkinson power divider for L-band applications is detailed in this article. The Wilkinson power is generated through the utilization of the parallel coupler's phase shifter. To showcase the potential of the suggested power divider, an FR4 microwave substrate is fabricated with a narrow band of 1.6 GHz to accommodate radio astronomy systems. The insertion loss, return loss, and isolation loss for the power divider's difference phase are accurately simulated at the operational frequency. © 2024 IEEE.

关键词： Radio astronomy

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A Novel Approximate Message Passing Detection for Massive MIMO 5G System

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computer Systems Science & engineering 2023年第6期45卷 2827-2835页

作者： Nidhi Gour Rajneesh Pareek Karthikeyan Rajagopal Himanshu Sharma Mrim M.Alnfiai Mohammed A.AlZain Mehedi Masud Arun Kumar Department of Computer Science&Engineering JECRC UniversityJaipurIndia Centre for Nonlinear Systems Chennai Institute of TechnologyChennaiIndia Department of Electronics and Communication Engineering and University Centre for Research&Development Chandigarh UniversityMohaliIndia Department of Information Technology College of Computer and Information TechnologyTaif UniversityP.O.Box 11099Taif21994Saudi Arabia Department of Computer Science College of Computers and Information TechnologyTaif UniversityP.O.Box 11099TaifSaudi Arabia Department of Electronics and Communication Engineering JECRC UniversityJaipurIndia

Massive-Multiple Inputs and Multiple Outputs(M-MIMO)is considered as one of the standard techniques in improving the performance of Fifth Generation(5G)radio.5G signal detection with low propagation delay and high throughput with minimum computational intricacy are some of the serious concerns in the deployment of *** evaluation of 5G promises a high quality of service(QoS),a high data rate,low latency,and spectral efficiency,ensuring several applications that will improve the services in every *** existing detection techniques cannot be utilised in 5G and beyond 5G due to the high complexity issues in their *** the proposed article,the Approximation Message Passing(AMP)is implemented and compared with the existing Minimum Mean Square Error(MMSE)and Message Passing Detector(MPD)*** outcomes of the work show that the performance of Bit Error Rate(BER)is improved with minimal complexity.

关键词： AMP MMSE MPD 5G

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Power Budget Calculation and Sensitivity Enhancement for N×25 Gbps TDM-PON Configuration 3

Power Budget Calculation and Sensitivity Enhancement for N×...

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3rd IEEE International Maghreb Meeting of the Conference on Sciences and Techniques of Automatic Control and computer engineering, MI-STA 2023

作者： Feituri, Ahmed B Abdullah, M.F.L. Swedan, Abdulhameed Almabrok Suliman, Hager A Universiti Tun Hussen Onn Malaysia Faculty of Electrical and Electronic Engineering Batu Pahat Malaysia The Electronic and Communication Research Center Applied Research and Development Authority Tripoli Libya University of Misurata Department of Computer Science Misurata Libya

ISBN: (纸本)9798350319897

Passive Optical Network (PON) standards relied on Time Division Multiplexing (TDM) and Wavelength Division Multiplexing (WDM) methods to maximize data rate. The latest 25GS-PON specifications were declared by Multi-Source Agreement (MSA) group in 2022. The current work stacks two and four 25-Gb/s TDM-PON downstream (DS) channels. The simulation, evaluation, and outcomes are completed using various versions of *** software. O-band wavelength plan as well as Continuous-Wave (CW) laser with Non-Return-to-Zero/On-Off-Keying (NRZ-OOK) format support the system to mitigate Chromatic Dispersion (CD) and upgrade the system capacity. The power budget of 31.1 dB is suitable for Optical Distribution Network (ODN) standards, and the standard value of receiver sensitivity (-24.1 dBm) is achieved. The transmitted power of 1, 4, and 7 dBm are needed for 32, 64, and 128 users/λ, respectively. The transmission is successful for 512 subscribers and 20 km optical reach without the use of amplifiers, Forward Error Correction (FEC), and compensation techniques. The effect of four-wave mixing (FWM) is solved by the wavelength space of 0.8 nm as well as the slight increase in transmitted power from 7 to 7.8 dBm. The open and clear eye diagrams prove the quality of received signals. The simplicity of structure is high because of utilizing basic components and Intensity Modulation/Direct Detection (IM/DD) strategy without amplification and equalization. FEC and compensation techniques are recommended for future work. © 2023 IEEE.

关键词： Passive optical networks

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AI-Powered Multi View Face Video Super-Resolution Techniques for Real-Time Video Processing 2

AI-Powered Multi View Face Video Super-Resolution Techniques...

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2nd IEEE International Conference on Integrated Intelligence and communication Systems, ICIICS 2024

作者： Manjunatha, D.V. Maindola, Meenakshi Jose, Reny Kaliappan, S. Patel, Mitul Maranan, Ramya Department of Electronics & Communication Engineering Alvas Institute of Engineering & Technology Moodabidri India Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun India Idukki India Division of Research and Development Lovely Professional University Phagwara India Faculty of Engineering and Technology Parul institute of Engineering and Technology Parul University Vadodara India Department of Research and Innovation Saveetha School of Engineering SIMATS Chennai India

ISBN: (纸本)9798331504960

In order to improve the visual quality of low-resolution video frames, this study introduces a new superresolution method for real-time video processing that is powered by artificial intelligence. With little computational overhead, the suggested approach uses a hybrid deep learning model to reconstruct high-resolution frames. This model mixes Convolutional Neural Networks (CNNs) with Generative Adversarial Networks (GANs). The suggested method outperforms state-of-the-art methods by 12.3% in PSNR and 8.7% in SSIM, according to experimental results on benchmark video datasets. The PSNR is 34.56 dB and the SSIM is 0.94. Also, running on a GTX 1080Ti GPU, the system shows an average processing speed of 30 fps, which is great for real-time apps. The suggested strategy is effective in eliminating artifacts, improving visual clarity, and retaining fine details in various real-time video processing contexts, according to both quantitative and qualitative results. © 2024 IEEE.

关键词： Convolutional neural networks

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Image Processing Based Early Breast Cancer Detection in Mammography Images Using GRU and XGBoost Approach 1

Image Processing Based Early Breast Cancer Detection in Mamm...

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1st IEEE International Conference on Electronics, Computing, communication and Control Technology, ICECCC 2024

作者： Pandey, Abhishek Ramesh, V. Mohan, Ravi Kaliappan, S. Swetha Reddy, A. Gurunathan, K. SCSVMV University Department of Computer Applications Kanchipuram India Shri Ram Institute of Technology Department of ECE Madhya Pradesh Jabalpur India Lovely Professional University Division of Research and Development Punjab India R.M.K. College of Engineering and Technology Department of Electronics and Communication Engineering Chennai India Mount Zion College of Engineering and Technology Department of Computer Science and Engineering Pudukkottai India

ISBN: (纸本)9798350371802

One out of every eight women will be affected by breast cancer at some point in their lives, making it one of the most common malignancies in females. The most reliable method for finding breast cancer in its early stages is mammography. Due to the low visibility and weak contrast of mammographic photos, early identification of breast cancer is crucial for effective therapy. As a result of several computer-aided detection approaches, radiologists are now able to make more precise diagnoses. Operating Sequencing is crucial for several tasks, including morphology, preprocessing, segmentation, feature selection, and training models. The suggested method makes use of preprocessing technologies that can enhance mammography, remove noise, and identify pectoral muscles. Using image processing techniques, a digital image can be 'segmented' into many layers. Acquiring a precise image of the mass is the goal of the morphological process. Geometric algorithms are employed by the feature selection technique. Training a GRU-XGBoost model requires careful consideration of which features to use. The proposed method outperforms the current gold standard algorithms, GRU and XGBoost, in every respect. The results showed a remarkable improvement, with an accuracy of 95.63%. © 2024 IEEE.

关键词： Diseases

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CNN-Based Deep Learning Model for Early Identification and Categorization of Melanoma Skin Cancer Using Medical Imaging

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SN computer Science 2024年第7期5卷 911页

作者： Pandimurugan, V. Ahmad, Sultan Prabu, A.V. Rahmani, Mohammad Khalid Imam Abdeljaber, Hikmat A. M. Eswaran, Malathi Nazeer, Jabeen Computer Science and Engineering Acharya Institute of Technology Bangalore India Department of Computer Science College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz University Alkharj 11942 Saudi Arabia University Center for Research and Development (UCRD) Department of Computer Science and Engineering Chandigarh University Gharuan Punjab Mohali 140413 India Department of Electronics and Communication Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram AP Guntur 522502 India College of Computing and Informatics Saudi Electronic University Riyadh Saudi Arabia Department of Computer Science Faculty of Information Technology Applied Science Private University Amman Jordan MEU Research Unit Middle East University Amman 11831 Jordan Department of Computer Technology Kongu Engineering College Perundurai Tamil Nadu Erode India

In human life, skin cancer is a curse. If not appropriately diagnosed, it spreads around all body parts in the earlier stage. The melanoma skin cancer death rate is 75% all over the world. There is an urgent need for a cure to be in place. Swarm and evolutionary computation for healthcare deals with real-world healthcare applications. One of the best ways to do this is to find the genes that need to be turned off such that only cancer cells can die. The current therapies used to cure cancer focus on turning off cancer cells but, at times, affect healthy tissues. Using the power of Artificial Intelligence, one can easily mark out the cancer-affected area and provide treatment accordingly. Segmentation techniques can be used to mark cancer lesions, and advanced deep learning algorithms in computer vision can help classify which class of cancer lesions. Automatic detection of melanoma by using a dermoscopic sample is tricky to find the stage and percentage of lesions affected by using deep convolutional neural networks with the help of machine vision tools. To predict skin lesions, we developed a model with three layers, each having output channels of 16, 32, and 64, respectively. For this research, different samples were collected from the international skin imaging collaboration database (ISIC2019, ISIC2020, ISIC2021). The study computed the most important parameters for classifying and identifying the lesion with accuracy, precision, recall, and specificity values. The proposed DCNN model classifier attained an accuracy of 88.82%, 93.45%, and 95.15% with the respective datasets of ISIC2019, ISIC2020, and ISIC2021, indicating high performance compared with the other state-of-the-art networks. The proposed model is a better framework for automating early detection and classification of melanoma skin cancer to protect many lives and avoid mishaps. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Classification Convolution neural network Deep learning Medical imaging Melanoma Skin cancer Swarm and evolutionary computation

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Assessing the Performance of Interference-Aware Routing Protocols in Wireless Mesh Networks 15

Assessing the Performance of Interference-Aware Routing Prot...

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15th International Conference on Computing communication and Networking Technologies, ICCCNT 2024

作者： Balaji, N.V. Grover, Madhur Vignesh, T. Seth, Kanika Gopalakrishna, K. Kolekar, Vikas K. Karpagam Academy of Higher Education Department of Computer Science Coimbatore641021 India Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura140417 India Prince Shri Venkateshwara Padmavathy Engineering College Department of Computer Science and Engineering Chennai127 India Chitkara University Chitkara Centre for Research and Development Himachal Pradesh 174103 India School of Engineering and Technology Electronics and Communication Engineering Karnataka Bangalore India Vishwakarma Institute of Technology Department of Computer Engineering Pune India

ISBN: (纸本)9798350370249

The motive of this technical summary is to have a look at the performance of interference-conscious routing protocols for wireless Mesh Networks (WMNs). Interferenceconscious routing models help to lessen interference among WMN nodes associated with transmissions at the identical frequency channel. This type of interference can degrade the throughput of a network and reduce communication performance. The efficacy of a routing protocol is typically measured via parameters consisting of packet transport ratio, throughput, and end-to-stop delay. This AC will compare and assess unique algorithms for interference-conscious routing and evaluate their overall performance in terms of these parameters. Initial simulations will be carried out on diverse topologies of WMNs to determine the effect of interference-aware routing protocols on packet delivery ratio, throughput, and stop-to-stop put-off. The outcomes may be used to put together an assessment among exceptional algorithms and determine the most effective technique. In addition, the abstract will discover approaches to optimize the layout of interference-conscious routing protocols to attain the most green conversation overall performance. Subsequently, the paper will finish with a dialogue of capability demanding situations and important future studies guidelines in this area. © 2024 IEEE.

关键词： Mesh generation

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engineering Educators' Adoption and implementation of Project-Based Learning: Experiences from a South Indian University 13

Engineering Educators' Adoption and implementation of Projec...

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13th IEEE International Conference on Teaching, Assessment and Learning for engineering, TALE 2024

作者： Dijo, Anjal P Anantha, Krishnan K. P. Subheesh, N.P. John, Aby Anzar, S.M. Sachidananthan, Sajin Federal Institute of Science and Technology Department of Computer Science and Engineering India College of Engineering Department of Computer Science and Engineering Kerala India Srm University - Ap Teaching Learning Centre Andhra Pradesh 522502 India Rudn University Faculty of Philology Moscow Russia Tkm College of Engineering Department of Electronics and Communication Engineering Kollam India Curriculum Research and Development U-DNA Educational Foundation Andhra Pradesh Anantapur India

ISBN: (纸本)9798350376234

This research investigates the attitudes and perceptions of engineering educators in Kerala, India, towards the implementation of Project-Based Learning (PjBL) within the framework of APJ Abdul Kalam Technological University (APJAKTU), Kerala. Despite the growing emphasis on experiential learning, only limited research studies focus on understanding the attitudes and perceptions of engineering faculty members towards PjBL. Against this backdrop, the present study aims to critically examine the readiness of engineering educators in adopting and identifying PjBL strategies for its effective integration into engineering curricula. The paper explores various dimensions of engineering educators' engagement with PjBL, including faculty interest levels, satisfaction of implementation, perceived effectiveness, and career implications. The data were collected from engineering faculty members by distributing a comprehensive questionnaire titled Project-Based Learning in engineering Education in Kerala (PLEEK), uniquely designed by the researchers. The research findings reveal that over 90% of the surveyed faculty members have shown keen interest in implementing PjBL, while the remaining respondents exhibited a neutral stance. This underscores a noteworthy receptivity to PjBL among engineering educators in Kerala. Suggestions from participants indicate that, if implemented correctly, PjBL would be highly effective, although time constraints pose a significant challenge. The results obtained are pivotal as they provide an explicit idea of the current status of the engineering education settings and the discrepancies between traditional theoretical instruction and project-based learning. © 2024 IEEE.

关键词： engineering education

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A Detailed Analysis of Speculative Based Algorithm Implementation for Modish House 4

A Detailed Analysis of Speculative Based Algorithm Implement...

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4th IEEE International Conference on Advance Computing and Innovative Technologies in engineering, ICACITE 2024

作者： Shah, Sanjeev Kumar Kumar, N. Siva Gurumoorthi, E. Shnain, Ammar Hameed Kumar, Ashok Nandhini, T.J. Uttaranchal University Uttaranchal Institute of Technology Department of Electronics & Communication Engineering Dehradun248007 India Aditya Engineering College Department of Computer Science and Engineering Surampalem India Cmr College of Engineering & Technology Department of Cse India The Islamic University Najaf Iraq Chitkara University Chitkara Centre for Research and Development Himachal Pradesh 174103 India Department of Computer Science and Engineering Chennai Tamil Nadu Thandalam602105 India

ISBN: (纸本)9798350360165

In the abruptly evolving vicinity of clever homes, the integration of quick-variety devices through the Internet of Things (IoT) affords an exceptional opportunity and a massive task. The variety of tool communique protocols and the absence of popular production requirements have historically impeded seamless integration, necessitating a sturdy answer. This paper introduces the smart home framework, a progressive IoT-based designed to bridge the gap amongst a massive range of brief-variety devices and man or integration platforms. Leveraging the OSGi ultra-modern, smart allows key protocols along with Zigbee, EnOcean, KNX, X10, Z-Wave for tool verbal exchange, and CoAP (Constrained Application Protocol), MQTT (Message Queuing Telemetry Transport) and XAMPP ((CrossPlatform, Apache, MySQL, PHP, Perl) for application interaction. This framework stands out for its adaptability, extensibility, and the potential to significantly streamline and enhance smart domestic ecosystems. © 2024 IEEE.

关键词： Smart homes

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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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