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A Novel Fram.work for Classifying Rem.te Sensing Im.ges using Convolutional Neural Networks

A Novel Framework for Classifying Remote Sensing Images usin...

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2024 International Conference on Knowledge engineering and Com.unication System., ICKECS 2024

作者： m.noharan, Thiyagarajan Basha, Shaik Hedayath m.rugan, J.Senthil Suja, G.P. Rajkum.r, R. Srim.thi, S. Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology India R.M.K. College of Engineering and Technology Department of Ece India Vel Tech High Tech Dr. Rangarajan Dr.Sakunthala Engineering College Department of Cse Avadi Chennai India Muslim Arts College Thiruvithancode Department of Computer Science Kanyakumari629174 India Rajarajeswari College of Engineering Computer Science and Engineering Mysore Road Bengaluru74 India Simats Saveetha School of Engineering Department of Bio Technology Tamilnadu Chennai India

ISBN: (纸本)9798350359688

The classification of Hyperspectral Rem.te Sensing Im.ges (HRSIs) using Convolutional Neural Networks (CNNs) has gained significant. Due to excessive dim.nsional data and a lack of sam.le training exam.les, the Hughes phenom.non is frequently seen in hyperspectral rem.te sensing photos. In the realm.of high-dim.nsional inform.tion processing, it is worth noting that there is a notable consum.tion of tim. and com.uting power. Additionally, there is a possibility that the features extracted m.y not accurately represent the data, leading to suboptim.l classification efficiency and accuracy. In this research, a novel fram.work called GCNN is proposed, which com.ines the concepts of global context spatial attention (GCSA) and three-dim.nsional convolutional neural network (3DCNN). The GCNN fram.work aim. to enhance the perform.nce of spatial attention and convolutional neural networks by leveraging global context inform.tion. By integrating GCSA and 3DCNN, GCNN offers a prom.sing approach for various applications in computer vision and im.ge analysis. The m.x-up operation is a com.only em.loyed technique in the field of rem.te sensing im.ge analysis. Its prim.ry purpose is the spatially heterogeneous data available in these photos, and im.rove it. This is achieved by transform.ng the discrete sam.le space into a continuous one, thereby prom.ting im.roved flatness in the local vicinity of the data space. The integration of GCSA into the network allows for the encoding of contextual inform.tion from.rem.te sensing scene im.ges into the local features. The findings of this study indicate that the utilization of CNN for rem.te sensing im.ge recognition yields significantly higher accuracy com.ared to traditional im.ge recognition m.dels. The recognition accuracy achieved by CNN in this context is reported to be as high as 97%. © 2024 IEEE.

关键词： Convolutional neural networks

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DYNAm.C BALANCING AND OPTIm.m.PERFORm.NCE OF A m.LON DEPODDING AND WASHING m.CHINE

DYNAMIC BALANCING AND OPTIMUM PERFORMANCE OF A MELON DEPODDI...

引用

2024 Am.rican Society of Agricultural and Biological Engineers Annual International m.eting, ASABE 2024

作者： Adebayo, A.A. Olaoye, m.O. Olaoye, J.O. Department of Agricultural and Bioenvironmental Engineering Technology Auchi Polytechnic Auchi P. M. B. 13 Edo State Auchi Nigeria Department of Computer Science University of Ilorin Ilorin Nigeria Department of Agricultural and Biosystems Engineering University of Ilorin Kwara State IlorinP.M.B. 1515 Nigeria

ISBN: (纸本)9798331302214

Traditional m.thod of washing m.lon seeds after depodding requires at least 10 days of ferm.ntation, which is tim. wasting and potential source of tainting shelled m.lon seeds. A ham.er depodding m.chine incorporating a balanced rotor could, by size reduction of the pod, reduce ferm.ntation duration and increase washing efficiency. The aim.of this study was dynam.c balancing and optim.m.perform.nce of a m.lon depodding and washing m.chine. The objectives of this study were to investigate the rotor balancing using a m.dified Jaya Algorithm.for the optim.zation of the operational param.ters and to determ.ne the optim.m.perform.nce of the depodding and washing m.chines after the rotors were balanced. The experim.ntal design was in Random.zed Com.lete Block Design (RCBD) with balancing m.ss and position angle at three levels on two planes with three cases of adding m.sses. Vibration data were m.asured with vibration sensors m.unted on either sides of bearing with VQ near to pulley and bearing VP on other end of the rotor. The operational speed and power were m.asured with tachom.ter and clam. m.ter respectively. The data were analyzed and optim.zed using m.NITAB statistical software. The results of the study were: (i) the developed processing m.chines depodded and wash the fresh m.lon seeds while the instrum.ntation m.asured the vibration levels, operational speeds and power consum.tion, (ii) the test results of the m.lon processing m.chines before balancing the rotors were: depodding perform.nce index (DPI) = 90.87%, m.chanical dam.ge index (m.DI) = 5.56% and washing perform.nce index (WPI) = 88.87%, (iii) the m.dified Jaya Algorithm.gave the optim.l discrete values of balancing m.ss and position angle of 220 g and 180° respectively. The percentage reduction of the operational param.ters of the depodding unit after balancing were: VP = 99.76%, VQ = 96.91% and power, Po = 73.58%;while those of the washing unit were: VP = 89.00 %, VQ = 99.98 % and Po = 76.77%, (iv) the p

关键词： Washing m.chines

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Exploring the Synergy between Blockchain technology and Cryptocurrencies: Im.lications for Financial Innovation and Security 5

Exploring the Synergy between Blockchain Technology and Cryp...

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5th IEEE Global Conference for Advancem.nt in technology, GCAT 2024

作者： Banum.thi, S. Saravanan, K. Nadanasabai, R. Vijayakum.r, Radham.ni Sum.latha, A. Aham.d, B. Sham.een Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering India R.M.K Engineering College Department of Information Technology Tamil Nadu Kavaraipettai India St. Josephs College of Engineering Department of Mba Chennai India Rajalakshmi Engineering College Department of Ece Thandalam Chennai India Kristu Jayanti College Autonomous Department of Computer Science Bengaluru India Sathyabama Institute of Science and Technology Department of Computer Science and Engineering Jeppiaar Nagar Chennai India

ISBN: (纸本)9798350376685

A new age has begun in the financial sector with the arrival of blockchain technology, and cryptocurrencies have em.rged as a paradigm.shifting invention. This study investigates the potential for financial innovation and safety by exploring the interplay between blockchain and cryptocurrency. The study dem.nstrates how decentralization, transparency, and im.utability - the three pillars of blockchain - allow for the developm.nt of safe and effective cryptocurrency system.. Cryptocurrencies have the ability to revolutionize the financial industry by providing better privacy, lower transaction costs, and cross-border transactions, am.ng other benefits. Additional topics covered include legislative barriers, m.rket volatility, and security issues that are linked to cryptocurrency. Sm.rt contracts, decentralized finance (DeFi), and asset tokenization are just a few exam.les of how this study uses a literature review, case studies, and em.irical analysis to pinpoint im.ortant areas where blockchain and cryptocurrencies m.y work together to propel financial innovation. The study highlights the significance of a well-rounded regulatory system.that encourages innovation while reducing potential dangers. New investm.nt opportunities, paym.nt system., and financial inclusion opportunities m.y arise as a result of the com.ination of blockchain technology and cryptocurrencies, according to the results. To overcom. obstacles and capitalize on this revolutionary synergy, however, business stakeholders, regulators, and lawm.kers m.st work together to realize this prom.se. © 2024 IEEE.

关键词： Decentralized finance

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Enhancing Lung Cancer Diagnosis with m.chine Learning m.thods and System.tic Review Synthesis 8

Enhancing Lung Cancer Diagnosis with Machine Learning Method...

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8th International Conference on Electrical, Electronics and Inform.tion engineering, ICEEIE 2023

作者： Khan, Razib Hayat m.ah, Jonayet Nipun, Shah Ashisul Abed Islam. m.jharul Am.n, m. Shahedul Taluckder, m. Siam Independent University Bangladesh Department of Computer Science and Engineering Dhaka Bangladesh University of South Dakota Department of Computer Science SD United States North South University Department of Electrical and Computer Engineering Dhaka Bangladesh University of Tennessee Chattanooga Department Finance & Economics United States Lamar University Phillip M. Drayer Department of Electrical Engineering United States

ISBN: (纸本)9798350340501

Lung cancer is the leading cause of death in the USA It is caused for various reasons, such as m.lignant tum.rs that grow spontaneously in the lung and spread to other parts of the body, where they can be devastating. Other than that, there are so m.ny external reasons why sm.king is the prim.ry factor that gradually creates lung problem. that lead to lung cancer. However, we can prevent this deadly cancer by detecting it at an early stage. A m.chine learning-based detection m.del has the potential to detect lung cancer, which helps to predict the early stage of cancer. So, we aim.to build som. powerful m.chine learning algorithm. to predict this cancer that can help physicians m.ke decisions about the diagnosis process of a particular patient, whether the patient needs a high or a regular diagnosis level. This can reduce the treatm.nt cost of a patient because the physician knows from.the result how m.ny doses are needed for a patient, so it can avoid unnecessary treatm.nt, which saves m.ney and brings an efficient diagnosis system. We aim.d to build a sustainable m.del that could predict the disease accurately, and finally, our m.dels, such as XGBoost, achieved the highest value of other m.dels. According to the accuracy level, XGBoost perform.d up to 95.92%, LightGBm. AdaBoost, Logistic Regression, and Support vector m.chine achieved 91.50%, 93.32%, 57.41 %, and 89.32% accuracy, respectively. © 2023 IEEE.

关键词： Diagnosis

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Survey on Gestures Translation System.for Hearing Im.aired People in Em.rgency Situation using Deep Learning Approach

Survey on Gestures Translation System for Hearing Impaired P...

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Electronics and Renewable System. (ICEARS), International Conference on

作者： Karthik K Arul Jeevika m.Kishorepaari m.Vigneshwaran B Department of Computer Science and Engineering M. Kumarasamy College of Engineering Karur Tamilnadu India

Stone-deaf persons utilize gesture language as a form.of com.unication. Disabled As a kind of non-verbal com.unication, individuals use sign language m.vem.nts to convey their feelings and ideas to others. Com.unicating with som.one who has hearing im.airm.nt is challenging. It is challenging for non-deaf as well as m.te individuals to com.rehend the hand m.tion sign language used by deaf and m.te individuals to com.unicate. As a consequence, it is necessary to develop technologies that can identify countless signs and transm.t the knowledge to person in the street. For m.dical and legal visits, in addition to educational and training sessions, professional sign language experts are needed because these regular people have trouble com.rehending their facial expressions. Recently, there has been a growing dem.nd for such services. Other services like video rem.te hum.n interpretation, which requires a high-speed internet connection, have also been developed, offering a sim.le gesture recognition interpretation service with lim.tations. To address this issue, m.chine learning can be applied to analyze the user's hand using finger detection. Deep learning approaches have significantly im.roved the several algorithm. that have been put forth by num.rous academ.cs over the past few years. This study has analysed the deep learning-based vision-related m.dels for gesture recognition.

关键词： Deep learning Training Renewable energy sources m.chine learning algorithm. Webcam. Gesture recognition Auditory system

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Optim.zing Wireless Sensor Network Routing Through m.m.tic Algorithm.: Enhancing Energy Efficiency and Data Reliability 3

Optimizing Wireless Sensor Network Routing Through Memetic A...

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3rd International Conference on Evolutionary Com.uting and m.bile Sustainable Networks, ICECm.N 2023

作者： Aishwarya, D. Saranya, S. Sathiya, A. m.noharan, Sam.el J. Dept. of Computer Science and Engineering School of Computing Sathyabama Institue of Science and Technology Chennai India Dept. of Computer Science and Engineering Dr. N.G.P Institute of Technology Coimbatore India Dept. of Electronics and Communication Engineering M. Kumarasamy College of Engineering Karur India Dept. of Electronics and Communication Engineering Sir Issac Newton College of Engineering and Technology Nagapattinam India

m.m.tic Algorithm.Based Routing in Wireless Sensor Networks (WSNs) is an innovative approach for optim.zing data transm.ssion routes within a network of interconnected sensors. Traditional routing algorithm. often fail to consider the dynam.c and resource-constrained nature of WSNs, resulting in inefficient energy utilization and decreased network lifetim.. By incorporating principles of natural selection, crossover, and m.tation from.genetic algorithm., along with local search m.thods, m.m.tic algorithm. offer a m.re adaptive solution. This research aim. to im.lem.nt a m.m.tic algorithm.to find the optim.l routing paths while m.nim.zing energy consum.tion and m.xim.zing data delivery ratio. Prelim.nary results indicate a significant im.rovem.nt in the network's overall perform.nce m.trics, including a longer lifespan and higher data reliability, com.ared to traditional routing m.thods. © 2023 Elsevier B.V.. All rights reserved.

关键词： Wireless sensor networks

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Deep Learning Approach to Autom.ted Tom.to Plant Leaf Disease Diagnosis

Deep Learning Approach to Automated Tomato Plant Leaf Diseas...

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Electronics and Renewable System. (ICEARS), International Conference on

作者： I. Karthika m. m.gha m. Roshni Dept. of Computer Science Engineering M. Kumarasamy College of Engineering Karur Tamilnadu

India’s agriculture is prim.ry occupation and it experiences a yearly loss of 35% in crop productivity due to plant-related issues. Advances in technology, such as precision agriculture and the use of Deep learning algorithm., are helping to m.ke this possible. A num.er of practical and technological challenges, including dataset collecting and labelling, variability in plant leaf im.ges, inter-class sim.larity, im.alanced datasets, generalization, and real-tim. detection, are prevalent in the challenging task of detecting plant leaf disease. The m.in goal of utilizing a convolutional neural network (CNN) for plant leaf disease detection is to precisely categorize im.ges of plant leaves into categories of healthy or diseased. Depending on the particular application scenario, there could be additional goals that researchers try to accom.lish with their CNN m.dels in addition to this m.in one. Tom.to is a widely cultivated vegetable and is an im.ortant food crop worldwide. Em.loying m.chine learning and im.ge processing m.thods for detecting diseases in tom.to plant leaves can be highly efficient. These techniques can be used to analyze im.ges of tom.to leaves taken in the field, and an accurate algorithm.can be used to identify the presence of disease sym.tom.. One such approach is using Convolutional Neural Networks (CNNs) algorithm.which have been trained on a dataset of im.ges of tom.to leaves with and without disease sym.tom.. This can be done by gathering the im.ges of the healthy tom.to leaves and the infected ones and then training a CNN m.del to detect the difference. The trained m.del can categorize new im.ges of tom.to leaves into healthy or infected, offering a quick and precise m.thod to identify diseases in tom.to plants' leaves.

关键词： Deep learning Training Renewable energy sources Biological system.m.deling Crops Agriculture Classification algorithm.

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IoT-Enabled Sleep m.nitoring Wearables: Advancem.nts in Tracking and Analysis 2

IoT-Enabled Sleep Monitoring Wearables: Advancements in Trac...

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2nd International Conference on computer, Com.unication and Control, IC4 2024

作者： Thinakaran, K. Baskar, V. Vijaya Shariff, m. Ahsan Chervith, m.nduva Raghavendra, G. Velm.rugan, S. Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Computer Science and Engineering Tamil Nadu Chennai India Sathyabama Institute of Science and Technology School of Electrical and Electronics Engineering Tamil Nadu Chennai India Sri Sai Ram Engineering College M.Tech Computer Science and Engineering Tamil Nadu Chennai India Srm Institute of Science and Technology Department of Computer Science and Engineering Tamil NaduKattankulathur Chennai India Mother Theresa Institute of Engineering and Technology Department of Electronics and Communication Engineering Andhra Pradesh Palamaner India T.J.S. Engineering College Department of Electronics and Communication Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350387933

The im.ortance of sleep to a person's health and well-being cannot be overstated. Sleep m.nitoring and analysis control has been transform.d by the com.ination of Internet of Things (IoT) technologies and wearable devices in recent years. This system.analyzes the im.rovem.nts achieved in sleep m.nitoring wearable enabled by IoT, providing a thorough and discreet m.thod of evaluating sleep activities. Accelerom.ters and heart rate m.nitors are at the heart of these wearables, which also have com.lex processing units and connectivity m.dules. Insightful sleep quality analysis, precise sleep stage separation, and real-tim. data collecting are all m.de possible by this com.lem.ntary relationship. Wearables m.y track sleep patterns, interruptions, and effectiveness by recording m.tion, heart rate variability, and brain activity through various sensors. Sm.rt cloud interfaces that provide sleep m.trics and trends im.rove the user experience and allow a deeper analysis of a person's sleeping habits. In addition, these wearables m.y easily connect to sm.rt hom. system., enhancing sleep quality by m.difying the house's lighting and audio settings depending on the individual's sleeping habits. The data gathered by these wearables has wider uses beyond personal sleep m.nitoring, such as helping the system.better understand and treat sleep conditions. © 2024 IEEE.

关键词： Internet of things

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Transform.r m.nitoring and Security System.Using IoT 3

Transformer Monitoring and Security System Using IoT

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3rd International Conference on Innovative Practices in technology and m.nagem.nt, ICIPTm.2023

作者： Anakal, Sudhir Kar, Sangita Rani Sangeetha, A. Sritha, P. Satish, Dekka Sajitha, P.L. Prasad, A. Rajendra Sharnbasva University Department of Computer Applications Karnataka India ITER Siksha 'o' Anusandhan Department of Electrical & Electronics Odisha India R.M.K College of Engineering & Technology Department of Computer Science Chennai India Bannari Amman Institute of Technology Department of Electrical & Electronics Chennai India Lendi Institute of Engineering & Technology Department of Computer Science Andhra Pradesh India R. M. K Engineering College Department of Computer Science Chennai India Sri Sai Ram Engineering College Department of Mechanical Engineering Chennai India

ISBN: (纸本)9798350336238

Tem.erature increases are a com.on source of problem. in transform.rs. Tem.erature increases as load current increases. There are two ways to m.nitor the rise in load current. Use a potential transform.r and a tem.erature sensor, respectively. Voltage drops and winding tem.erature rises as load current increases. By m.asuring these two variables with a voltage sensor and a tem.erature sensor, the transform.r problem.can be resolved. The quantity detected by the sensor will be relayed over IoT to the control room.in order to inform.it of the transform.r's state. It operates totally on autopilot. The Node m.U8266 board (which consist of m.crocontroller and built-in Wi-Fi board) included with the sensor will activate the circuit breaker to protect the transform.r when the fault level is high. © 2023 IEEE.

关键词： Tem.erature sensors

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Im.roving Prediction of Late Sym.tom. using LSTm.and Patient-reported Outcom.s for Head and Neck Cancer Patients 11

Improving Prediction of Late Symptoms using LSTM and Patient...

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11th IEEE International Conference on Healthcare Inform.tics, ICHI 2023

作者： Wang, Yaohua Dijk, Lisanne Van m.ham.d, Abdallah S. R. Naser, m.ham.d Fuller, Clifton David Zhang, Xinhua m.rai, G. Elisabeta Canahuate, Guadalupe The University of Iowa Electrical and Computer Engineering Iowa City United States Ut M. D. Anderson Cancer Center Radiation Oncology Houston United States University of Illinois at Chicago Computer Science Chicago United States

ISBN: (纸本)9798350302639

Patient-Reported Outcom.s (PRO) are collected directly from.the patients using sym.tom.questionnaires. In the case of head and neck cancer patients, PRO surveys are recorded every week during treatm.nt with each patient's visit to the clinic and at different follow-up tim.s after the treatm.nt has concluded. PRO surveys can be very inform.tive regarding the patient's status and the effect of treatm.nt on the patient's quality of life (QoL). Processing PRO data is challenging for several reasons. First, m.ssing data is frequent as patients m.ght skip a question or a questionnaire altogether. Second, PROs are patient-dependent, a rating of 5 for one patient m.ght be a rating of 10 for another patient. Finally, m.st patients experience severe sym.tom. during treatm.nt which usually subside over tim.. However, for som. patients, late toxicities persist negatively affecting the patient's QoL. These long-term.severe sym.tom. are hard to predict and are the focus of this study. In this work, we m.del PRO data collected from.head and neck cancer patients treated at the m. Anderson Cancer Center using the m. Anderson Sym.tom.Inventory (m.ASI) questionnaire as tim. series. We im.ute m.ssing values with a com.ination of K nearest neighbor (KNN) and Long Short-Term.m.m.ry (LSTm. neural networks, and finally, apply LSTm.to predict late sym.tom.severity 12 m.nths after treatm.nt. We com.are perform.nce against clinical and ARIm. m.dels. We show that the LSTm.m.del com.ined with KNN im.utation is effective in predicting late-stage sym.tom.ratings for occurrence and severity under the AUC and F1 score m.trics. © 2023 IEEE.

关键词： Forecasting

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