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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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Utilization of ECC in Smart Grids: A Review

Utilization of ECC in Smart Grids: A Review

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2023 International Conference on Power Energy, Environment and Intelligent Control, PEEIC 2023

作者： Gopala Krishna, K. Shekhawat, Jyoti Agarwal, Trapty Faculty of Engineering and Technology Department of Electronics and Communication Engineering Bangalore India Vivekananda Global University Department of Computer Science & Application Jaipur India Maharishi University of Information Technology Engineering Department Uttar Pradesh India

ISBN: (纸本)9798350357769

Within the realm of smart grids, a power service provider plays a crucial function in coordinating predicted power production with actual power demand in order to support power system stability. Insufficient protection means that, even with these advancements, data sent inside smart grids is still vulnerable to a variety of security risks and assaults. To enhance the security of electronic grid networks, a robust and effective authentication procedure must be put in place. The trouble of ensuring the security of communications among substations and electronic machines stays, even in shrewd lattice settings where SCADA (supervisory controls and data acquisition) frameworks assume a part in shielding associations between the administration place and substations. In this present circumstance, predominant security strategies frequently neglect to accomplish an unpretentious harmony among security and execution. With the expansion of an safe part on the smart machine side, this work incorporates security insurance into a moderation verification framework in view of elliptic bend cryptography. This clever convention handles the requirement for a finely tuned balance between the security and execution parts of shrewd lattices. Key arrangement, common verification, and character security are significant parts of the proposed convention. The convention's viability is upheld by a complete assessment utilizing the Gong-Needham-Yahalom rationale. This procedure is a critical improvement in reinforcing the security position of smart grids, particularly taking into account the continually changing climate of cyber threats and weaknesses. © 2023 IEEE.

关键词： Authentication Elliptic Curve Cryptography Privacy Protection SCADA Systems Security Smart Grids

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A Novel Hybrid Evolutionary Mating Algorithm for Covid19 Confirmed Cases Prediction based on Vaccination

A Novel Hybrid Evolutionary Mating Algorithm for Covid19 Con...

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2023 International Conference on Next-Generation Computing, IoT and Machine Learning, NCIM 2023

作者： Ahmed, Marzia Mohamad, Ahmad Johari Rahman, Mostafijur Sulaiman, Mohd Herwan Kashem, Mohammod Abul Faculty of Electrical & Electronics Engineering Technology Pahang Malaysia Daffodil International University Department of Software Engineering Dhaka Bangladesh Green University of Bangladesh Department of Computer Science and Engineering Bangladesh DUET Department of Computer Science and Engineering Gazipur Bangladesh

ISBN: (纸本)9798350316001

Microorganisms may cause illness when they enter the body, multiply, and spread to other parts. The rapid spread of COVID-19 to neighboring countries is examined in this research. Anticipating a positive COVID-19 occurrence helps in determining risks and creating countermeasures. As a result, developing robust mathematical models with small error margins for predictions is crucial. Based on these findings, a combined method of evaluating confirmed cases of COVID-19 with universal immunization is recommended. First, the best hyperparameter values of the RBF kernel-based LSSVM (least square support vector machine) were determined using the most recent Evolutionary Mating Algorithm (EMA). After that, LSSVM will complete the task of prediction. This hybrid method has been utilized for time series forecasting in Malaysia since the country's immunization program against COVID-19 got underway. We evaluate our results next to those of well-known methodologies in nature-inspired metaheuristics. © 2023 IEEE.

关键词： COVID-19

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Hybrid Machine Learning Approach for Enhanced Vulnerability Detection in Cloud Environments Using NIST and MITRE Frameworks 5

Hybrid Machine Learning Approach for Enhanced Vulnerability ...

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5th International Conference on Advanced Research in Computing, ICARC 2025

作者： Lasantha, N W Chanaka Maduranga, M.W.P. Abeysekara, Ruvan Tilwari, Valmik Chakraborty, Nilotpal Sharma, Debashree Iic University of Technology Faculty of Graduate Studies Phnom Penh Cambodia University of Sri Jayewardenepura Department of Electrical and Electronic Engineering Nugegoda Sri Lanka Bcas Campus Faculty of Computing Colombo Sri Lanka Indian Institute of Information Technology Department of Computer Science Engineering Guwahati India Indian Institute of Information Technology Department of Electronics & Communication Engineering Guwahati India

ISBN: (纸本)9798331530983

Cloud security is challenged by constant adaptive cyber threats and traditional detection methods lack real time adaptability. In this paper, we propose a new hybrid ML approach stitching data from National Institute of Standards and technology (NIST) and MITRE Adversarial Tactics, Techniques and Common Knowledge (ATT&CK) databases together. In contrast to existing methods, this framework combines, in unique and unprecedented fashion, ensemble ML models and Realtime data processing for increased detection accuracy and reduced false positives. This approach addresses specific challenges in the cloud environments and by leveraging standardised frameworks, it offers actionable audiences for efficient and effective vulnerability management process. The framework is validated by the experimental results which prove its robustness to evolution of the cybersecurity demands. © 2025 IEEE.

关键词： Cyber attacks

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Design and Analysis of Graphene Based Tunnel Field Effect Transistor with Various Ambipolar Reducing Techniques

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computers, Materials & Continua 2023年第7期76卷 1309-1320页

作者： Puneet Kumar Mishra Amrita Rai Nitin Sharma Kanika Sharma Nitin Mittal Mohd Anul Haq Ilyas Khan ElSayed M.Tag El Din Department of Electronics and Communication Engineering G L Bajaj Institute of Technology and ManegmentGretear NoidaIndia Department of Electronics and Communication Engineering Chandigarh UnievrsityMohaliPunjabIndia Department of Electronics and Communication Engineering NITTTRChandigarhIndia University Center of Research&Development Chandigarh UniversityMohali121102India Department of Computer Science College of Computer and Information SciencesMajmaah UniversityAl-Majmaah11952Saudi Arabia Department of Mathematics College of Science Al-ZulfiMajmaah University11952Al-MajmaahSaudi Arabia Electrical Engineering Department Faculty of Engineering&TechnologyFuture University in EgyptNew Cairo11835Egypt

The fundamental advantages of carbon-based graphene material,such as its high tunnelling probability,symmetric band structure(linear dependence of the energy band on the wave direction),low effective mass,and characteristics of its 2D atomic layers,are the main focus of this research *** impact of channel thickness,gate under-lap,asymmetric source/drain doping method,workfunction of gate contact,and High-K material on Graphene-based Tunnel Field Effect Transistor(TFET)is analyzed with 20 nm *** modelling and electrical characteristic performance have been simulated using the Atlas device simulator of SILVACO TCAD with user-defined material syntax for the newly included graphene material in comparison to silicon carbide(SiC).The simulation results in significant suppression of ambipolar current to voltage characteristics of TFET and modelled device exhibits a significant improvement in subthreshold swing(0.0159 V/decade),the ratio of Ion/Ioff(1000),and threshold voltage(-0.2 V with highly doped p-type source and 0.2 V with highly doped n-type drain)with power supply of 0.5 V,which make it useful for low power digital applications.

关键词： Graphene tunnel field effect transistor(TFET) band to band tunnelling subthreshold swing

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Robust Deep Learning Empowered Real Time Object Detection for Unmanned Aerial Vehicles based Surveillance Applications

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Journal of Mobile Multimedia 2023年第2期19卷 451-476页

作者： Ranjith, C. Prasanna Hardas, Bhalchandra M. Khaja Mohideen, M. Syed Nijil Raj, N. Robert, Nismon Rio Mohan, Prakash Faculty in Information Technology Department University of Technology and Applied Sciences Shinas Oman Electronics Engineirng Dept Shri Ramdeobaba College of Enginering and Management Nagpur India Department of Information Technology University of Technology and Applied Science Salalah Oman Department of Computer Science and Engineering Younus College of Engineering and Technology Kollam India Department of Computer Science Christ University Bangalore India School of Computer Science and Engineering Vellore Institute of Technology Vellore India

Surveillance is a major stream of research in the field of Unmanned Aerial Vehicles (UAV), which focuses on the observation of a person, group of people, buildings, infrastructure, etc. With the integration of real time images and video processing approaches such as machine learning, deep learning, and computer vision, the UAV possesses several advantages such as enhanced safety, cheap, rapid response, and effective coverage facility. In this aspect, this study designs robust deep learning based real time object detection (RDL-RTOD) technique for UAV surveillance applications. The proposed RDL-RTOD technique encompasses a two-stage process namely object detection and objects classification. For detecting objects, YOLO-v2 with ResNet-152 technique is used and generates a bounding box for every object. In addition, the classification of detected objects takes place using optimal kernel extreme learning machine (OKELM). In addition, fruit fly optimization (FFO) algorithm is applied for tuning the weight parameter of the KELM model and thereby boosts the classification performance. A series of simulations were carried out on the benchmark dataset and the results are examined under various aspects. The experimental results highlighted the supremacy of the RDL-RTOD technique over the recent approaches in terms of several performance measures. © 2022 River Publishers.

关键词： computer vision deep learning image processing object detection Surveillance unmanned aerial vehicles

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CPW-fed Monopole Antenna with 3.5/5.5 GHz Band-Notched Characteristics for Ultrawideband Applications

CPW-fed Monopole Antenna with 3.5/5.5 GHz Band-Notched Chara...

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2024 International Symposium on Antennas and Propagation, ISAP 2024

作者： Tangthong, Nipont Moeikham, Pichet Sangpet, Patchadaporn Jamnien, Atthakorn Akkaraekthalin, Prayoot Chudpooti, Nonchanutt Deparment of Electronics and Telecommunication Engineering Faculty of Engineering Rajamangala University of Technology Thanyaburi Pathum Thani Thailand Deparment of Electrical Engineering Faculty of Engineering Rajamangala University of Technology Lanna Chiang Rai Chiang Rai Thailand KMUTNB Techno Park King Mongkut's University of Technology North Bangkok Bangkok Thailand Deparment of Electrical and Computer Engineering Faculty of Engineering King Mongkut's University of Technology North Bangkok Bangkok Thailand Deparment of Industrial Physics and Medical Instrumentation Faculty of Applied Science King Mongkut's University of Technology North Bangkok Bangkok Thailand

ISBN: (纸本)9798350364774

This research presents a printed ultrawideband monopole antenna designed with notches at the 3.5 GHz and 5.5 GHz frequency bands. The antenna incorporates a 50-Ω Coplanar Waveguide (CPW) feed, featuring a gradual central line and ground plane. To notch unwanted frequencies at 3.5 GHz and 5.5 GHz, an inverted triangle slot and an inverted U-shaped slot are integrated into the monopole patch. Operating across a frequency range of 2.62-12 GHz, the proposed antenna supports the UWB system while excluding the 3.22-3.62 GHz and 5.14-5.62 GHz bands. The lengths of the inverted triangle and U-shaped slots correspond to quarter and half wavelengths at 3.5 GHz and 5.5 GHz, respectively. The prototype has been fully developed and tested, showing its effectiveness in reducing Electromagnetic Interference (EMI) in WLAN/WiMAX bands. © 2024 IEEE.

关键词： Monopole antennas

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Adversarial Neural Network Classifiers for COVID-19 Diagnosis in Ultrasound Images

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computers, Materials & Continua 2022年第1期70卷 1683-1697页

作者： Mohamed Esmail Karar Marwa Ahmed Shouman Claire Chalopin Department of Industrial Electronics and Control Engineering Faculty of Electronic Engineering(FEE)Menoufia UniversityMenouf32952Egypt Department of Computer Engineering and Networks College of Computing and Information TechnologyShaqra UniversityShaqra11961Saudi Arabia Department of Computer Science and Engineering Faculty of Electronic Engineering(FEE)Menoufia UniversityMenouf32952Egypt Innovation Center for Computer Assisted Surgery(ICCAS) Universit鋞LeipzigLeipzig04103Germany

The novel Coronavirus disease 2019(COVID-19)pandemic has begun in China and is still affecting thousands of patient livesworldwide *** X-ray and Computed Tomography are the gold standardmedical imaging modalities for diagnosing potentially infected COVID-19 cases,applying Ultrasound(US)imaging technique to accomplish this crucial diagnosing task has attracted many physicians *** this article,we propose two modified deep learning classifiers to identify COVID-19 and pneumonia diseases in US images,based on generative adversarial neural networks(GANs).The proposed image classifiers are a semi-supervised GAN and a modifiedGANwith auxiliary *** one includes a modified discriminator to identify the class of the US image using semi-supervised learning technique,keeping its main function of defining the“realness”of tested *** tests have been successfully conducted on public dataset of US images acquired with a convex US *** study demonstrated the feasibility of using chest US images with two GAN classifiers as a new radiological tool for clinical check of COVID-19 *** results of our proposed GAN models showed that high accuracy values above 91.0%were obtained under different sizes of limited training data,outperforming other deep learning-based methods,such as transfer learning models in the recent ***,the clinical implementation of our computer-aided diagnosis of US-COVID-19 is the future work of this study.

关键词： COVID-19 medical imaging machine learning applications adversarial neural networks ultrasound

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Developing a computer-Aided Diagnostic System for Breast Cancer Ultrasound Imaging

Developing a Computer-Aided Diagnostic System for Breast Can...

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2023 IEEE EMBS Special Topic Conference on Data Science and engineering in Healthcare, Medicine and Biology, IEEECONF 2023

作者： Taha, Radwa Afifi, Shereen Abd-Elghany, Mohamed A. Salem, Mohammed A.-M German University in Cairo The Faculty of Media Engineering and Technology New Cairo Egypt German University in Cairo Faculty of Media Engineering and Technology Computer Science Engineering New Cairo Egypt German University in Cairo Faculty of Information Engineering and Technology Electrical Engineering New Cairo Egypt Tu Darmstadt Integrated Electronics Systems Lab Germany German University in Cairo Faculty of Media Engineering and Technology Digital Media Engineering New Cairo Egypt

ISBN: (纸本)9798350383386

In this research we aim to develop a computer-aided diagnostic system (CAD) for breast ultrasound imaging to enhance the early detection of breast cancer. CAD tools aid physicians in the diagnostic process leading to early detection and treatment of breast cancer. The proposed CAD tool comprises three stages, the first is enhancement of breast ultrasound imaging by applying custom segmentation deep learning architectures [U-Net, SK-U-Net, RDA-U-Net] to enhance the isolation of masses in Ultrasound images, then generation of new ultrasound images by employing Generative Adversarial Networks [DAGAN, DCGAN] to overcome data limitation in available data-sets, and the last stage is the detection and classification of breast cancer masses by a CNN model. The enhancement stage shows promising results with accuracy of 98 % overall accuracy, 92 % overall precision, and 85 % overall *** Relevance: Breast cancer is one of the deadly diseases in the world. The earlier the detection the higher the chances of recovery. Thus a CAD tool that facilitates the screening process of ultrasound images will aid physicians in the early detection and diagnosis of the disease. © 2023 IEEE.

关键词： Diseases

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Enhancing Quality for VVC Compressed Videos with Omniscient Quality Enhancement Model

Enhancing Quality for VVC Compressed Videos with Omniscient ...

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2024 International Conference on Advanced Technologies for Communications, ATC 2024

作者： Hoangvan, Xiem Minh, Hieu Bui Nguyenquang, Sang Peng, Wen-Hsiao University Of Engineering and Technology Vietnam National University Faculty of Electronics and Telecommunications Hanoi Viet Nam National Yang Ming Chiao Tung University Department of Computer Science Hsinchu Taiwan

ISBN: (纸本)9798350353983

The latest video coding standard H.266/VVC has shown its great improvement in terms of compression performance when compared to its predecessor HEVC standard. Though VVC was implemented with many advanced techniques, it still met the same challenges as its predecessor due to the need for even higher perceptual quality demand at the decoder side as well as the compression performance at the encoder side. The advancement of Artificial Intelligence (AI) technology, notably the deep learning-based video quality enhancement methods, was shown to be a promising approach to improving the perceptual quality experience. In this paper, we propose a novel Omniscient video quality enhancement Network for VVC compressed Videos. The Omniscient Network for compressed video quality enhancement was originally designed for HEVC compressed videos in which not only the spatial-temporal features but also cross-frequencies information were employed to augment the visual quality. Inspired by this work, we propose a modification of the OVQE model and integrate it into the lasted STD-VVC (Standard Versatile Video Coding) decoder architecture. As assessed in a rich set of test conditions, the proposed OVQE-VVC solution is able to achieve significant PSNR improvement, notably around 0.74 dB and up to 1.2 dB with respect to the original STD-VVC codec. This also corresponds to around 19.6% of bitrate saving while keeping a similar quality observation. © 2024 IEEE.

关键词： Decoding

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