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2023 Intelligent Computing and Control for engineering and Business Systems, ICCEBS 2023

作者： Sundravadivelu, K. Senthilvel, P. Gururama Thirupurasundari, D.R. Rajesh Kumar, K. Palani, Hari Kumar School of Information Technology Madurai Kamaraj University Department of Computer Science Tamil Nadu Madurai625021 India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering India Bharath Institute of Higher Education and Research Department of Computer Science and Engineering Tamil Nadu Chennai India Madha Engineering College Department of Electronics and Communication Engineering Chennai Tamilnadu Kundrathur India School of Engineering Ajeenkya D y Patil University India

ISBN: (纸本)9798350394580

Accurate and scalable plant health monitoring has entered a new age with the combination of automated drone-based imaging systems with cutting-edge deep learning algorithms. This cutting-edge method uses drones' mobility and adaptability to get detailed photographs, which reveal previously unattainable information on plant health and disease. We have made great progress in identifying plant illnesses, nutritional deficits, and growth irregularities using state-of-the-art deep-learning algorithms. To analyze drone video in real-time, our approach uses convolutional neural networks (CNNs) trained on large datasets of annotated plant images. The ability to quickly diagnose and pinpoint the exact location of plant health problems is a major step forward in developing effective intervention techniques and improving agricultural practices. The system's ability to detect even minute differences in plant health has been greatly improved by incorporating multispectral imaging capabilities, which allows it to go beyond the bounds of simple visual spectral analysis. This study is a breakthrough in agricultural monitoring since it offers a practical, economical, and ecologically sound approach to the problem of ensuring the world's population has access to enough to eat. When combined with deep learning, automated drone-based imaging systems have the potential to completely transform plant health assessment, providing invaluable assistance to farmers, agronomists, and researchers as they work to increase crop yield while decreasing resource inputs and negative environmental effects. Insights into the full scope of this game-changing technology and its potential to profoundly alter the face of contemporary farming are presented in this research. © 2023 IEEE.

关键词： Deep Learning Techniques Drone-based Imaging Systems Plant Health Monitoring

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A Smart IoT Solution for Automatic Potholes and Humps Detection: Enhancing Road Safety

A Smart IoT Solution for Automatic Potholes and Humps Detect...

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International Conference on ent Computing and Control Systems (ICICCS)

作者： Dinesh Besiahgari Manjunath R Kounte Shivananda S University of Cincinnati Electronics and Computer Engineering School of Electronics and Communication Engineering REVA University Bengaluru India Department of Information Science and Engineering M S Ramaiah Institute of Technology Bengaluru India

ISBN: (数字)9798331512088

ISBN: (纸本)9798331512095

This paper outlines an IoT based smart system to improve road safety in developing countries by solving the problem of poorly maintained roads. The proposed solution uses ultrasonic sensors and Radio Frequency (RF) transmission system to detect potholes and humps and sends alarm signals to the drivers in real time through Blynk application. A GPS module is used to pin down the exact coordinates of these irregularities and the data is saved in a database which can be used by other drivers and road authority. Although this system presents a feasible and economical design, aspects such as the cost of implementation, robustness towards environmental factors and long term sustainability are analyzed in detail to determine the feasibility of the system. This work is intended to minimize vehicle break down and accidents resulting from road hazards and therefore provides a solution that can be scaled up for enhanced road safety.

关键词： Radio frequency Smart systems Sensor systems and applications Road safety Robustness Real-time systems Pins Internet of Things Sustainable development Vehicles

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MRI-3D Image Fusion Using an Automatic Cone-Centred Breast Ultrasound Scanner

MRI-3D Image Fusion Using an Automatic Cone-Centred Breast U...

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2023 IEEE International Conference on Paradigm Shift in Information Technologies with Innovative Applications in Global Scenario, ICPSITIAGS 2023

作者： Mustare, Narendra B Sivaji, U. Das, Debi Prasad Sivasangari, A. Ammisetty, Veeraswamy Harshitha, Doppalapudi Cvr College of Engineering Telangana State Hyderabad India Institute of Aeronautical Engineering Information Technology Hyderabad India Kiit School of Management Kiit University Bhubaneswar India Sathyabama Institute of Science and Technology Department: It India Koneru Lakshmaiah Education Foundation Department of Computer Science & Engineering Vaddeswaram India Kkr & Ksr Institute of Technology and Sciences Electronics and Communication Engineering India

ISBN: (纸本)9798350329773

The limits of conventional approaches are criticized and the importance of prompt detection of breast cancer with sophisticated imaging techniques is emphasized in this study report. The research supports the revolutionary potential of MRI-3D image fusion, which goes beyond the limitations of traditional modalities by fusing 3D MRI with computerized cone-centered breast ultrasonography. It explores multimodal image fusion techniques such as spatial registration and feature-based synthesis and shows how they might improve understanding of breast lesions. It is recognized that incorporating intelligent technology (AI) into combined image operations may automate procedures, increase accuracy, and decrease human error. Even with these developments, problems like false positives continue to arise, which is why neural network integration is being proposed. The study also looks at newer imaging modalities such PET and tomosynthesis, highlighting how they work together to provide a complete picture of breast tumors. The use of these innovative technologies must take into account ethical factors such as patient permission, privacy, and equitable access. The study ends by recommending ongoing investigation, commitment to ethical principles, and patient-centered treatment in order to mold a bright future for diagnostics related to breast health. © 2023 IEEE.

关键词： Image fusion

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Remaining Useful Life Estimation of Lenses for an Ion Beam Etching Tool in Semiconductor Manufacturing Using Deep Convolutional Neural Networks 13

Remaining Useful Life Estimation of Lenses for an Ion Beam E...

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13th International Conference on electronics, Communications and Networks, CECNet 2023

作者： Wan, Jian McLoone, Seán Department of Mechanical Biomedical and Design Engineering School of Engineering and Technology College of Engineering and Physical Sciences Aston University BirminghamB4 7ET United Kingdom School of Electronics Electrical Engineering and Computer Science Queen’s University Belfast BelfastBT9 5BN United Kingdom

ISBN: (纸本)9781643684802

Maintenance plays a significant role in semiconductor manufacturing as plant yield, factory downtime and operation cost are all closely related to maintenance efficiency. Accordingly, maintenance strategies in semiconductor manufacturing industries are increasingly shifting from traditional preventive maintenance (PM) to more efficient predictive maintenance (PdM). PdM uses manufacturing process data to develop predictive models for remaining useful life (RUL) estimation of key equipment components. Traditional approaches to building predictive models for RUL estimation involve manual selection of features from manufacturing process data. This paper proposes to use deep convolutional neural networks (CNN) for the task of estimating RUL of lenses for an ion beam etch tool in semiconductor manufacturing. The proposed approach has the advantage of automatic feature extraction through the use of convolution and pool filters along the temporal dimension of the optical emission spectroscopy (OES) data from the endpoint detection system. Simulation studies demonstrate the feasibility and the effectiveness of the proposed approach. © 2024 The authors and IOS Press.

关键词： Optical emission spectroscopy

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Adaptive PSO based gain optimization of sliding mode control for position tracking control of magnetic levitation systems

Adaptive PSO based gain optimization of sliding mode control...

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2022 International Conference on Information and Communication technology for Development for Africa, ICT4DA 2022

作者： Bosera, Adisu Safo Olana, Frew Dereje Merga, Chala Gutole, Siraj Tura Mettu University Department of Electrical and Computer Engineering Mettu Ethiopia Addis Ababa Institute of Technology School of Electrical and Computer Engineering Addis ababa Ethiopia Technical and Vocational Training Institute Department of Electrical and Electronics Technology Addis ababa Ethiopia

ISBN: (数字)9781665455879

ISBN: (纸本)9781665455879

In this paper, nonlinear adaptive particle swarm optimization-based gain optimization of sliding mode control systems is presented under matching model uncertainty and random Gaussian external disturbances for magnetic levitation position control systems. The main problems in magnetic levitation control systems are: lack of nonlinear control system;limited position control, i.e., up to 1mm only;convergence rate;accuracy of control;chattering problems in conventional SMC;and lack of considering the effects of system parameters and loading mass changes into account with external disturbance. In this paper, first, a third-order dynamic nonlinear model was created, which includes mechanical (ball position and velocity) and electrical (the current) subsystems with uncertainty in the system parameter and loading mass of 20% each. Secondly, a sliding mode control system for position control systems under both matched model uncertainty (internal) and external Gaussian disturbances is designed for magnetic levitation position tracking systems. Then, the gain of the sliding mode control system is allocated in such a way that the ITSE fitness function is minimal using the adaptive particle swarm optimization technique. The suggested control method, which is based on the combination of the proposed sliding mode control system and adaptive particle swarm optimization, offers control performance with a significant improvement in terms of chattering reduction using arc tangential function, high precision control accuracy, and fast convergence rate. Moreover, a robust optimal SMC controller designed for magnetic levitation systems under both internal and external disturbances is able to reject all disturbances. Finally, the algorithm's performance is demonstrated by model simulation and the proposed control, with simulation results indicating good convergence for given constant and constant plus sinusoidal reference positions. © 2022 IEEE.

关键词： Magnetic levitation

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LiFiData: A Secured Data Communication over Defense Fields by using Intelligent Visible Light Assisted Transmissions

LiFiData: A Secured Data Communication over Defense Fields b...

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2023 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems, ICSES 2023

作者： Chithra, L. Padma, S. Farzhana, I. Saravanan, L. Krishnakumari, S. S.A.Engineering College Department of ECE Chennai India Sona College of Technology Department of EEE Salem India Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of Computer Science and Engineering Chennai India PERI Institute of Technology Department of ECE Chennai India Loyola ICAM College of Engineering and Technology Department of Electronics and Communication Engineering Chennai India

ISBN: (纸本)9798350319200

The electronic industry is currently experiencing a transformation due to the rise of solid state devices. Light-Emitting-Diode (LED) are the quintessential solid-state gadget example. LEDs' are a viable alternative to both incandescent and fluorescent lighting. They produce little to no heat and can save you money. As an added bonus, LEDs have a far longer lifespan than conventional light bulbs. LEDs generate higher-quality light in terms of color temperature than both incandescent and fluorescent bulbs. In addition, they can rapidly cycle across a wide range of light intensities. This system must be safe, cheap, and environmentally benign in order to successfully initiate electronic communication utilizing visible light. The suggested system uses a Arduino microcontroller in the transmitting portion, where a high-brightness LED light serves as the channel for communication and a transistor, BC 547, is employed to transform 5v to *** a microcontroller receives data from a computer, it decrypts the information by first converting it to binary, and only then sending it to the led as a digital pulse. The lightbegins to blink as data is transmitted to the receiver;the photo diode begins to sense light, generating electric pulses;the amplifier processes these pulses into data;the data reaches the Arduino, where it can be viewed in the program's serial monitor. For this transmission, direct line-of-sight is essential;however interference from other sources can improve the results in a dark environment. In the lab, LiFi has reached very high speeds, and it also outperforms Wi-Fi in terms of bandwidth, efficiency, availability, and security. This study provides a clear, experimental perspective on the benefits of LiFi technology as well as its potential future applications. However, the hybrid model that incorporates the WiFi and LiFi technologies is expected to see significant growth in demand in the telecommunications industry. © 2023 IEEE.

关键词： Communication LED LiFi Security Visible Light Data Transmission VLC WiFi Wireless Communication

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Machine Learning Algorithm for Predicting Soil Classification using Smart Agriculture 3

Machine Learning Algorithm for Predicting Soil Classificatio...

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3rd International Conference on Sentiment Analysis and Deep Learning, ICSADL 2024

作者： Ponkumar, D. David Neels Kumar, Kolipaka Pushpa Krupa Himesh Ramesh, S. Kumar, J. Arun Nallasivan, G. Jayachandra, A. Vel Tech Rangarajan Dr. Sagunthala RandD Institute of Science and Technology Department of Electronics and Communication Engineering Tamil Nadu Chennai India SRM Institute of Science and Technology Department of Computing Technologies Tamil Nadu Chengalpattu India Vel Tech Rangarajan Dr. Sagunthala RandD Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350396157

The advent of Internet of Things (IoT) technology and data analytics has brought about a significant transformation in contemporary agricultural practices, sometimes referred to as smart agriculture. One of the fundamental uses of this technology is the prediction of soil classification, a critical factor in the optimization of crop management strategies and the allocation of resources. The present work investigates the use of machine learning techniques to achieve precise and efficient soil categorization in the context of smart agriculture. The study utilizes a comprehensive dataset consisting of several soil characteristics, including pH levels, moisture content, texture, and nutrient composition. These attributes were obtained via the use of Internet of Things (IoT) sensors and Unmanned Aerial Vehicles (UAVs). Several machine learning techniques, such as Random Forest, Support Vector Machine, and Neural Networks, are assessed in terms of the classification accuracy in identifying soil types using the given characteristics. The findings indicate that machine learning models has the capability to accurately forecast soil categorization, hence enabling the use of precision agricultural techniques. These models assist farmers in making informed choices based on data analysis pertaining to crop selection, irrigation, and fertilization, resulting in enhanced agricultural productivity and the adoption of sustainable resource management practices. Additionally, this research investigates the interpretability of the chosen machine learning models, with the aim of ensuring that farmers are able to grasp and place faith in the forecasts. The proper implementation of smart agricultural systems also involves addressing ethical problems related to data privacy and security. This study makes a valuable contribution to the progress of sustainable agriculture via the use of machine learning and Internet of Things (IoT) technology. By leveraging these technologies, the research a

关键词： Soils

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Simulative analysis of carrier suppressed return to zero based symmetrical compensated optical link

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Journal of Optical Communications 2024年

作者： Singh, Manjit Sarangal, Himali Singh, Butta Kour, Satveer Kaur, Pawandeep Department of Engineering and Technology Guru Nanak Dev University Regional Campus Jalandhar India Department of Computer Engineering and Technology GNDU Punjab Amritsar India Department of Electronics Technology GNDU Punjab Amritsar India

Optical communication systems provide high data rates to fulfil users' demands. In addition, the modulation format is essential to the system's transmission quality and spectral efficiency. Furthermore, the effects of chromatic dispersion, which impair the performance and quality of transmitted signals, are mitigated using dispersion compensation techniques. In optical communication systems, dispersion compensation techniques are essential for preserving signal quality, increasing transmission distances, improving system performance, and offering network designers flexibility. These methods seek to improve signal quality and enable transmission at longer distances by reducing or eliminating the distortion brought on by dispersion. This paper uses a symmetrical compensation technique to analyze the performance of the CSRZ modulation format-based optical communication system. The research is conducted within a communication range of 150-350 km. The system is evaluated in terms of input power, filter order, and filter depth. The results indicate that the proposed system performed better at an input power of 4 dBm using a third-order filter and 60 dB filter depth. © 2024 Walter de Gruyter GmbH, Berlin/Boston 2024.

关键词： Chromatic dispersion

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Temperature compensation for humidity sensors using ISSA-BP neural network 30

Temperature compensation for humidity sensors using ISSA-BP ...

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30th International Conference on Mobile Computing and Networking, ACM MobiCom 2024

作者： Li, Dapeng Zhang, Hechu Yang, Yu Li, Wei Wang, Shuai Zheng, Dezhi School of Instrumentation and Optoelectronic Engineering Beihang University Beijing China School of Electronics and Information Engineering Beihang University Beijing China Department of Mechanical Engineering University College London London United Kingdom School of Computer Science and Engineering Beihang University Beijing China Advanced Research Institute of Multidisciplinary Sciences Beijing Institute of Technology Beijing China

ISBN: (纸本)9798400704895

High-precision humidity detection is essential in various fields. However, the sensor's output signal is often affected by complex environmental temperature changes. To address these challenges, this paper propose an improved sparrow search algorithm based on the back propagation neural network (ISSA-BP). This method optimizes the initialization process of the traditional algorithm and introduces an edge evolution strategy to enhance its iterative update process, significantly improving both efficiency and accuracy. Experimental results demonstrate that the proposed algorithm reduces the mean absolute percentage error (MAPE) from 4.35% to 0.92% and decreases the convergence time from 1.25 s to 0.65 s, compared to traditional methods. © 2024 Copyright is held by the owner/author(s). Publication rights licensed to ACM.

关键词： Humidity sensors

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Twitter Sentiment Analysis using Ensemble Based Deep Learning Techniques for Analyzing COVID-19 Tweets

Twitter Sentiment Analysis using Ensemble Based Deep Learnin...

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2023 International Conference on Evolutionary Algorithms and Soft Computing Techniques, EASCT 2023

作者： Kumar, Bura Vijay Monica, K.M. Prabhakar, S. Perumal, Senthil Ramachandra, A.C. Sr University School of Computer Science and Artificial Intelligence Warangal India Vit School of Computer Science and Engineering Chennai India S.A Engineering College Department of Mathematics Chennai India SRMIST-Ramapuram Department of Ece Chennai India Nitte Meenakshi Institute of Technology Department of Electronics and Communication Engineering Bengaluru India

ISBN: (纸本)9798350313413

The COVID-19 is a transportable disease which is rapidly spread and numerous peoples are endures and died from this disease. This paper proposed an ensemble based deep learning techniques for examining COVID-19 tweets. The COVID19 Tweets dataset is utilized in this research which consist of 3100 tweets. The preprocessing is accomplished by including the activities like stemming, tokenization and stop words removal. The preprocessed data features are extracted through Team Frequency-Inverse Document Frequency (TF-IDF). Then, it classified through ensemble classifiers such as Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU) and Bidirectional RNN (BRNN). The model performance was evaluated through accuracy, precision, recall, sensitivity and f1-score. The obtained result shows that the ensemble classifier attains high accuracy of 0.985, precision of 0.982, recall of 0.971, specificity of 0.974 and f1-score of 0.978 which is comparatively higher than existing techniques like Extra Tree Classifier (ETC) and Heterogeneous Support Vector Machine (H-SVM). © 2023 IEEE.

关键词： Ensemble Classifier Gated Recurrent Unit Long Short-Term Memory Recurrent Neural Network Sentiment Analysis Twitter

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