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

作者： Velu, Anitha Ramamoorthy, Raghu Manasa, S.M. Navulkumar, Devakirubai Sri Sairam College of Engineering Department of Electronics and Communication Engineering Bengaluru India The Oxford College of Engineering Department of Computer Science and Engineering Bengaluru India Jayam College of Engineering and Technology Department of Computer Science and Engineering Dharmapuri India

ISBN: (纸本)9798350371802

Internet of Things (IoT) has radically improved and modernized in every aspect of human existence. The term IoT is a recent trend that states the development a self-configurable network by connecting a variety of hardware objects, to internet without requiring human input to improve service delivery and automate processes by means of data transmission to the cloud. Streetlights are an essential component of any city, although they provide safer roadways, improved night vision, and increased visibility of public spaces it uses a significant amount of electricity and operates at maximum intensity from dusk till dawn. The wastage of electricity can be secured by having lights turn OFF on their own if it is not in use. This work presents an energy efficient IoT-based smart street lighting by using low-cost System On Chip (SOC) circuit. This work deploys an LDR sensor which acts as a decision device, to choose whether to switch ON or OFF the street light based on the level of ambient light. This simple switch has a relay at its output and is activated by either light or dark. This switching may be done using a cost-effective Wi-Fi module namely, ESP8266 for which the LDR sensed value is given as input. Any user can check the status of a street light whether it is ON/OFF from anywhere using internet in real time. It promises great dependability and remarkable stability for long-term. The proposed work is implemented using a NodeMCU board that is designed to supply the required light intensity at different times. © 2024 IEEE.

关键词： Internet of things

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Enhancing MRI-Based Brain Tumor Classification Accuracy through EfficientNetB3 and Advanced Image Data Augmentation Techniques 2

Enhancing MRI-Based Brain Tumor Classification Accuracy thro...

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2nd IEEE International Conference on Emerging Research in Computational science, ICERCS 2024

作者： Vanitha, K. Mahesh, R.T. Aafiya Thahaseen, A. Manivannan, B. Anitha, K. Thakur, Arastu Faculty of Engineering Department of Computer Science and Engineering Coimbatore India Department of Computer Science and Engineering Karnataka Bangalore India Al-Ameen Engineering College Department of Computer Science and Engineering Erode India Vivekanandha College of Engineering for Women Department of Computer Science and Engineering India CSI College of Engineering Department of Information Technology Tamilnadu Ooty India

ISBN: (纸本)9798331534950

This work analyzes the possibilities of the EfficientNetB3 architecture, reinforced by modern image data augmentation methods, in the classification of brain cancers from MRI scans. Our key objective was to greatly boost the accuracy and reliability of brain tumor diagnosis by combining the advantages of a pre-trained EfficientNetB3 model, along with a custom-designed data augmentation strategy geared at effectively resolving the inherent variabilities in MRI images. To achieve this, comprehensive preprocessing protocols were applied to the MRI datasets designated for training, validation, and testing. These protocols included the resizing and normalization of images to ensure uniformity and compatibility with the neural network. Further, our approach utilized real-time image augmentation techniques such as rotations, shifts, and flips during the training process, thereby enhancing the model's exposure to a wide range of imaging scenarios and increasing its robustness against variations not present in the training set. The outcome of this research was a notable enhancement in the model's classification accuracy, achieving up to 99% accuracy in identifying various types of brain tumors. These results not only demonstrate the effectiveness of integrating sophisticated convolutional network architectures with extensive image augmentation in medical diagnostics but also suggest potential pathways for setting new standards in the accuracy and reliability of medical image analysis, particularly in the critical domain of brain tumor diagnosis. © 2024 IEEE.

关键词： Deep learning

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Optimizing breast cancer diagnosis: Harnessing the power of nature-inspired metaheuristics for feature selection with soft voting classifiers

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International Journal of Cognitive Computing in engineering 2025年第1期6卷 1-20页

作者： Benghazouani, Salsabila Nouh, Said Zakrani, Abdelali Department of Mathematics and Computer Science Faculty of Sciences Ben M'Sick Hassan II University Casablanca Morocco Department of Computer Science Engineering ENSAM Hassan II University Casablanca Morocco

Breast cancer is a widespread and serious condition that poses a significant threat to women's health globally, contributing significantly to mortality rates. Machine learning tools play a critical role in both the effective management and early detection of this disease. Feature selection (FS) methods are essential for identifying the most impactful features to improve breast cancer diagnosis. These methods reduce data dimensionality, eliminate irrelevant information, enhance learning accuracy, and improve the comprehensibility of results. However, the increasing complexity and dimensionality of cancer data pose substantial challenges to many existing FS methods, thereby reducing their efficiency and effectiveness. To overcome these challenges, numerous studies have demonstrated the success of nature-inspired optimization (NIO) algorithms across various domains. These algorithms excel in mimicking natural processes and efficiently solving complex optimization problems. Building on these advancements, we propose an innovative approach that combines powerful feature selection methods based on NIO techniques with a soft voting classifier. The NIO techniques employed include the Genetic Algorithm, Cuckoo Search, Salp Swarm, Jaya, Flower Pollination, Whale Optimization, Sine Cosine, Harris Hawks, and Grey Wolf Optimization algorithms. The Soft Voting Classifier integrates various machine learning models, including Support Vector Machines, Gaussian Naive Bayes, Logistic Regression, Decision Tree, and Gradient Boosting. These are used to improve the effectiveness and accuracy of breast cancer diagnosis. The proposed approach has been empirically evaluated using a variety of evaluation measures, such as F1 score, precision, recall, accuracy and Area Under the Curve (AUC), for performance comparison with individual machine learning techniques. The results demonstrate that the soft-voting ensemble technique, particularly when combined with feature selection based on the Jaya

关键词： Support vector machines

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Role of Machine Learning in Acute Stroke Imaging: A Technical Review 1

Role of Machine Learning in Acute Stroke Imaging: A Technica...

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1st International Conference on Advanced Computing and Emerging Technologies, ACET 2024

作者： Singh, Gurmeet Singh, Mahender Gupta, Akansha Singh, Deepjyot Department of Electronics and Communication Engineering New Delhi India Department of Computer Science and Engineering New Delhi India

ISBN: (纸本)9798350367720

The utilization of machine learning (ML) and pattern recognition algorithms, which is used for analysis of complex datasets to detect similarities, differences, or trends, which can be particularly useful in medical diagnostics, has become essential in enhancing the prognostics of various neurological diseases such as Acute Ischemic Stroke (AIS). These advanced techniques have particularly transformed the evaluation and exploitation of neuroimaging data in the assessment and management of Acute Ischemic Stroke (AIS). In addition, these techniques also enable rapid and accurate analysis of complex imaging data, aiding clinicians in timely diagnosis, personalized treatment planning, and outcome prediction for stroke patients. This paper focuses on a comprehensive examination of machine learning algorithm strategies to enhance the accuracy and effectiveness of predicting strokes. The author has explored and reviewed different ML algorithms and has also delved into stroke prevention to advance our understanding. © 2024 IEEE.

关键词： Adversarial machine learning

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A Hybrid U-Net with Active Contours for Plant Leaf Disease Segmentation and Classification 3

A Hybrid U-Net with Active Contours for Plant Leaf Disease S...

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3rd International Conference on Electronics and Renewable Systems, ICEARS 2025

作者： Sivamurugan, J. Naveen, G. Rajalakshmi, D. Reddy, P. Venkata Subba Taher, Shaik Deepak, Sannapu Madha Institute of Engineering and Technology Department of Computer Science and Engineering Chennai India Madha Institute of Engineering and Technology Department of Electronics and Communication Engineering Chennai India R.M.D. Engineering College Department of Computer Science and Engineering Thiruvallur India

ISBN: (纸本)9798331509675

The economic growth of a nation entirely depends upon the agriculture and agricultural products. In developing countries like India, agriculture is the primary source of income and its contributing 17% to the total GDP. There are plenty of factors lead to plant disease which impacts the quality and yield of plants. Though manual method of detection is time consuming and it may have chance for errors. This method is not enough to identify and limit the spread of plant disease. To establish an automated plant disease detection in farms will reduce the risk of plant disease and promotes real-time monitoring of crops in a daily basis. Artificial Intelligence (AI) took part in supporting farmers to get instant solution in selection of fertilizers, classifying the quality of agricultural products, weather prediction and soil nutrient level detection. In this study, we proposes a novel segmentation approach namely Hybrid U-Net with active contours to segment the disease affected portion on leaf which support farmers to identify disease at early stage. This study provides a comprehensive analysis of plant disease segmentation by proposed method with conventional approaches. The publically available dataset is chosen for this analysis and performance of conventional studies was compared. This study presented current trends of plant disease segmentation and several other image classification techniques. Experimental results evaluating that the proposed study improved segmentation better than conventional methods. © 2025 IEEE.

关键词： Wiener filtering

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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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Circuit Design using Quantum Dot Technology 1

Circuit Design using Quantum Dot Technology

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1st International Conference on Intelligent Systems and Computational Networks, ICISCN 2025

作者： Manjunath, T.C. Kuravatti, Prakash Rajarajeswari College of Engineering Department of Computer Science & Engineering Bangalore India Rajeev Institute of Technology Department of Electronics and Communication Engineering Hassan India

ISBN: (纸本)9798331529246

In this paper, the harnessing QCA for High-Performance, Low-Power Arithmetic Circuits w.r.t. the focusing on Multipliers and Square Circuits is presented. The background in relation to this work is presented next. Quantum-dot Cellular Automata (QCA) is an emerging technology in nano-electronics that offers promising advantages over traditional CMOS (Complementary Metal-Oxide-Semiconductor) technology, including low power consumption, high operational speeds, and exceptional integration capabilities. These features position QCA as a potential successor to CMOS in various applications. Multipliers, critical components in digital signal processing, and square circuits, essential for mathematical computations, significantly impact system throughput and power efficiency. This research focuses on designing QCA-based multiplier and square circuits to leverage these advantages. Methods, Results, and Concluding Remarks w.r.t. the work is given here. The QCA-based multiplier was constructed using binary adders and AND gates, while a comparative analysis was conducted between QCA and CMOS technologies to evaluate performance. The evaluation considered parameters such as cell count, area, and energy dissipation, utilizing the QCA Designer tool for validation. Results demonstrate that QCA designs offer significant improvements in terms of reduced power dissipation and area requirements compared to CMOS. These findings highlight the potential of QCA technology to enhance the efficiency and integration of critical components in digital systems, marking it as a transformative approach in nano-electronics. © 2025 IEEE.

关键词： CMOS integrated circuits

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OFDM-CFO and Resource Scheduling Algorithm Using Fuzzy Linear-CFO

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Intelligent Automation & Soft Computing 2023年第2期35卷 2513-2525页

作者： M.Prabhu B.Muthu Kumar Department of Electronics and Communication Anna UniversityChennai600025India Department of Computer Science and Engineering Syed Ammal Engineering CollegeRamanathapuram623502India

Orthogonal Frequency-Division Multiplexing(OFDM)is the form of a digital system and a way of encoding digital data across multiple frequency com-ponents that are used in telecommunication *** Frequency Offset(CFO)inaccuracy is a major disadvantage of *** paper proposed a fea-sible and elegant fuzzy-based resource allocation technique,that overcomes the constraints of the *** suggested Fuzzy linear CFO estimation(FL-CFO)not only estimates the CFO with increased precision but also allocates resources effectively,and achieves maximum utilization of dynamic *** sug-gested FL-CFO error estimation algorithm in OFDM systems employing 1-bit Quadrate errors ADC(1-bit QE)is utilized to extract the precise ***-ally,the base station(BS)manages the Resource Units(RU),which could be used to distribute resources in such a manner that the user requests are *** assign resources to a certain job,fuzzy rules are *** the residence duration exceeds the resource requirement time then the particular resources are provided to the highest-priority *** a result,the job performance will not be halted due to a lack of allocated *** metrics such as utilization,rate of failure,and life span,as well as energy consumption,are used to assess the pro-posed FL-CFO.

关键词： Orthogonal frequency division multiplexing(OFDM) carrier

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Comprehensive Investigation and Evaluation of an Indoor 3D System Performance Based on Visible Light communication 3

Comprehensive Investigation and Evaluation of an Indoor 3D S...

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3rd International Conference on Emerging Smart Technologies and Applications, eSmarTA 2023

作者： Faraj Al Khattat, Vailet Hikmat Ahmad Anas, Siti Barirah Saif, Abdu Faculty of Engineering Universiti Putra Malaysia Department of Computer and Communication Systems Engineering UPM Serdang Selangor 43400 Malaysia Taiz University Faculty of Engineering and It Department of Communication and Computer Engineering Yemen

ISBN: (纸本)9798350305333

The obvious and accelerator trend towards efficient green technology in a modern technological revolution time makes visible light communication (VLC) a solution key to meeting this growth. Besides the illumination function, VLC uses for data transmission at high speed with the lowest cost due to utilizing the infrastructure that already exists. As a result of VLC's multiple features, it had been used in the indoor positioning system (IPS) in many pieces of research to obtain a high accuracy without conflict with the Radio Frequency (RF) spectrum besides it providing high security from any penetrating. However, achieving good performance parameters is essential and fundamental in evaluating the effectiveness of any indoor system that many pieces of research neglected and concentrated more on the positioning accuracy aspect. This paper investigates and analyses the performance of the indoor system that is designed and developed based on a VLC and proves its effectiveness through a comprehensive evaluation. Signal-to-noise ratio (SNR) and path loss are the performance parameters that are investigated in this system by varying the transmitted power, incidence angles, and Lambertian mode number in the line of sight (LOS) scenario. The examined system consists of one light-emitting diode (LED) in a 3D typical room and one photodiode (PD) in different ten locations along the half-diagonal line towards the corner. Utilizing a single LED in this system is important to avoid the interference that occurs when utilizing multiple LEDs;besides, it is more convenient in the indoor environment. The obtained results by MATLAB simulation show the reliable and effective performance of the proposed developed VLC system design by achieving a good SNR with low path loss. Furthermore, the proposed system approach proves how the optimum position of PD is crucial to obtain a strong signal with the lowest ratio of noise and losses. © 2023 IEEE.

关键词： Signal to noise ratio

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Simulation of fractional order mathematical model of robots for detection of coronavirus using Levenberg–Marquardt backpropagation neural network

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Neural Computing and Applications 2024年第35期36卷 22417-22428页

作者： Sabir, Zulqurnain Ali, Mohamed R. Sadat, R. Department of Computer Science and Mathematics Lebanese American University Beirut Lebanon Basic Engineering Mathematics Department Benha Faculty of Engineering Benha University Benha Egypt Faculty of Engineering Benha National University Obour Campus11835 Egypt Department of Mathematics Zagazig Faculty of Engineering Zagazig University Zagazig Egypt

This research motive is to present more realistic results through the fractional order derivatives of the Robots mathematical system (FORMS), which is used to detect the coronavirus-positive cases. This nonlinear FORMS is useful to prevent individuals from the coronavirus and its spread. The classification of mathematical system is categorized into two dynamics, infected and Robots. The designed FORMS has never been solved before through the stochastic Levenberg–Marquardt backpropagation (LBMBP) neural networks (NNs), i.e., LBMBP-NNs. The solution of three cases of the FORMS is presented along with the statistics of 76% training, 12% authorization and 12% testing. To observe the exactness of LBMBP-NNs, a reference dataset is constructed using the Adams scheme. For the validation and capability of LBMBP-NNs, the illustrations are drawn based on the state transition values, regression measures, correlation performances and error histograms. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Coronavirus

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