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A Text Image Watermarking Algorithm Based on Image Enhancement

computers, Materials & Continua 2023年第10期77卷 1183-1207页

作者： Baowei Wang Luyao Shen Junhao Zhang Zenghui Xu Neng Wang School of Computer Nanjing University of Information Science and TechnologyCollaborative Innovation Center of Jiangsu Atmospheric Environment and Equipment TechnologyDigital Forensics Engineering Research Center of Digital Forensics Ministry of EducationNanjing210044China School of Software Nanjing University of Information Science and TechnologyNanjing210044China

Digital watermarking technology is adequate for copyright protection and content *** needs to be more research on the watermarking algorithm after printing and *** at the problem that existing anti-print scanning text image watermarking algorithms cannot take into account the invisibility and robustness of the watermark,an anti-print scanning watermarking algorithm suitable for text images is *** algorithm first performs a series of image enhancement preprocessing operations on the printed scanned image to eliminate the interference of incorrect bit information on watermark embedding and then uses a combination of Discrete Wavelet Transform(DWT)-Singular Value Decomposition(SVD)to embed the *** show that the average Normalized Correlation(NC)of the watermark extracted by this algorithm against attacks such as Joint Photographic Experts Group(JPEG)compression,JPEG2000 compression,and print scanning is above ***,the average NC of the watermark extracted after print scanning attacks is greater than 0.964,and the average Bit Error Ratio(BER)is 5.15%.This indicates that this algorithm has strong resistance to various attacks and print scanning attacks and can better take into account the invisibility of the watermark.

关键词： Print-resistant scanning image enhancement DWT SVD embedding intensity

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Accurate Prediction of Electric Vehicle Battery Lifespan Using XGboost and Ensemble Methods 2

Accurate Prediction of Electric Vehicle Battery Lifespan Usi...

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

作者： Sathishkumar, S. Yogesh Rajkumar, R. Bharath Institute of Higher Education and Research Department of Computer Science and Engineering Chennai India Bharath Institute of Higher Education and Research Department of Information Technology Chennai India

ISBN: (数字)9798331534967

ISBN: (纸本)9798331534950

Thanks to their low running costs and positive environmental impacts, electric cars, or EVs, are growing in popularity. Still, the limited battery life of EVs is one of the primary drawbacks. The present work offers a thorough method for estimating the Nickel-Manganese Cobalt-Lithium Cobalt Oxide (NMC-LCO) batteries' Remaining Useful Life (RUL). Selection of features and outlier removal are the next steps in the multi-step process that begins with data gathering and preprocessing. The development of the RUL prediction model makes use of ML methods such as XG-Boost, Bagging-Regressor, Light-GBM and Cat-Boost. Critical factors affecting battery longevity and health may be found with the use of feature importance analysis. Analytical analyses show that there are no missing or duplicate data points, and the elimination of outliers improves model accuracy. XGBoost stood up as the most efficient algorithm, yielding almost flawless predictions. In applications like as electric cars, where resource optimisation, cost effectiveness, and environmental sustainability are critical, this study emphasises the value of RUL prediction in improving battery lifetime management. © 2024 IEEE.

关键词： Lithium batteries

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Battery Life-time Optimization Model (BLO) using Machine Learning Methods for Electric Vehicles 2

Battery Life-time Optimization Model (BLO) using Machine Lea...

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

ISBN: (数字)9798331534967

ISBN: (纸本)9798331534950

Effective and sustainable battery management is essential, as seen by the growing demand for batteries in electric cars and portable electronics. This study explores the use of machine learning approaches in battery research in a number of areas, including materials discovery, performance prediction, and remaining usable life (RUL) calculation. The rapid development of electric vehicle (EV) technology highlights the critical importance of lithium-ion batteries and intensifies the emphasis on sustainable energy storage. Using refined Machine Learning (ML) techniques like Random Forest (RF) and Support Vector Machine (SVM), this study mainly demonstrates how to predict the Remaining Useful Life (RUL) of lithium-ion batteries. Here, a new clustering-based method for predicting RUL is used to propose the Battery Life-Time Optimisation Model (BLO), which improves the accuracy of battery lifetime projections. Additionally, the development of next-generation batteries is aided by machine learning-driven materials discovery, which speeds up the identification of promising materials with exceptional attributes. Battery performance, sustainability, and dependability may all be significantly increased by using machine learning into battery research. The findings highlight machine learning's promise as a significant technique for resolving battery technology issues and opening the door to a more sustainable energy future. © 2024 IEEE.

关键词： Adversarial machine learning

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Environmental water quality prediction based on COOT-CSO-LSTM deep learning

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Environmental Science and Pollution Research 2024年第42期31卷 54525-54533页

作者： Rajagopal, Sankarasubbu Ganesh, Sundaram Sankar Karthick, Alagar Sampradeepraj, Thangavel Department of Information Technology National Engineering College Tamilnadu Kovilpatti628503 India Department of Computer Science and Engineering R.M.K. College of Engineering and Technology Puduvoyal Tamilnadu Thiruvallur601206 India Renewable Energy Lab Department of Electrical and Electronics Engineering KPR Institute of Engineering and Technology Tamilnadu Coimbatore641407 India Department of Computer Science and Engineering School of Computing Kalasalingam Academy of Research and Education Krishnankoil Tamilnadu Virudhunagar626126 India

Water resource management relies heavily on reliable water quality predictions. Predicting water quality metrics in the watershed system, including dissolved oxygen (DO), is the main emphasis of this work. The enhanced long short-term memory (LSTM) model was suggested to improve the model’s performance. Additionally, a hybrid model was employed to calculate the ideal parameter values for the LSTM model, which helped overcome the nonstationarity, unpredictability, and nonlinearity of the data about the water quality parameters. This model recruited the COOT method. The original weekly water quality values at the Vaigai River, Madurai, Tamil Nadu, India, were tested using the suggested hybrid model. An independent LSTM, the hybrid optimisation method takes its cues from the cuckoo bird’s reproductive strategy and a novel meta-heuristic optimisation technique dubbed COOT, which is based on the behaviour of a flock of coot birds. If implemented, the suggested hybrid model might serve as an alternate framework for water quality prediction, laying the groundwork for basin-wide efforts to manage water quality and control pollutants. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Macroinvertebrates

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The Future of Online Shopping: Blockchain- Powered E-Commerce Platforms

The Future of Online Shopping: Blockchain- Powered E-Commerc...

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2024 International Conference on Signal Processing, Computation, Electronics, Power and Telecommunication, IConSCEPT 2024

作者： Kiran, Ajmeera Sri, Nemani Divya Ayyasamy, Ramesh Kumar Chinnasamy, P. Vellanki, Sreelakshmi Vujwala, Maddula MLR Institute of Technology Department of Computer Science and Engineering Hyderabad India Faculty of Information and Communication Technology Kampar Malaysia School of Computing Kalasalingam Academy of Research and Education Department of Computer Science and Engineering Tamilnadu India

ISBN: (纸本)9798331540685

A cutting-edge online marketplace that uses blockchain technology to transform how we purchase and sell goods and services is known as a blockchain- powered e-commerce platform. This platform uses a decentralized network of computers, also known as nodes, to maintain a transparent and impenetrable record of all transactions instead of depending on a central authority like a bank or a company. A blockchain-based e-commerce platform is a secure, transparent, and trusted online marketplace. It does away with the need for intermediaries, improves security, and provides a world market where users can confidently conduct peer-to-peer transactions. This novel strategy could change how online commerce and shopping are performed. © 2024 IEEE.

关键词： Marketplaces

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Exploring Alzheimer's Disease Risk Prediction: A Data-Driven Approach with RandomForestClassifier 8

Exploring Alzheimer's Disease Risk Prediction: A Data-Driven...

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8th International Conference on Inventive Systems and Control, ICISC 202

作者： Jampani, Pallavi Javvadi, Varun Raj Nandi, Varun Sai Tanuku, Sai Varun Amarendra, K. Benarji, Bejjam S. N. Koneru Lakshmaiah Education Foundation Department of Computer Science and Information Technology Andhra Pradesh Vaddeswaram India Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Andhra Pradesh Vaddeswaram India

ISBN: (纸本)9798350386578

In this work, we employ a sophisticated machine learning technique based on the RandomForestClassifier model to examine Alzheimer's disease risk prediction. The '***' and '***' full datasets, which comprise both cross-sectional and longitudinal data, serve as the basis for our study. Our research project's major purpose is to create and assess a prediction model that may identify a person's chance of getting Alzheimer's disease based on clinical and demographic characteristics. Our method entails substantial data preparation, including feature engineering, imputation of missing values, and normalization. Feature engineering is a discrete stage. Stratified cross-validation approaches are used to train and modify the RandomForestClassifier model, establishing the validity of the performance metrics. Several important results from our work include evaluations of model correctness, a thorough assessment of prediction abilities, and insights into critical components that contribute to the risk assessment of Alzheimer's disease. In this chapter, we underline how machine learning may increase Alzheimer's disease research and clinical practice, and we analyze the implications of our results for early detection and therapeutic possibilities. This paper contributes a large quantity of information to the growing corpus in the realm of Alzheimer's disease prediction. It illustrates the advantages of data-driven methods and the importance of applying powerful machine learning algorithms in order to enhance healthcare results. © 2024 IEEE.

关键词： Clinical research

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Comparative Analysis of Machine Learning Algorithms for Early Detection of Chronic Kidney Disease: Performance Evaluation and Insights 3

Comparative Analysis of Machine Learning Algorithms for Earl...

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3rd International Conference on Smart Technologies and Systems for Next Generation Computing, ICSTSN 2024

作者： Joan Niveda, J. Yogesh Rajkumar, R. Bharath Institute of Higher Education and Research Department of Computer Science and Engineering Chennai India Bharath Institute of Higher Education and Research Department of Information Technology Chennai India

ISBN: (纸本)9798350391565

Chronic Kidney Disease (CKD) is a major global public health issue, highlighting the importance of early detection to slow down its advancement. This study conducts a comparative analysis of machine learning algorithms aimed at early CKD detection, focusing on performance evaluation and insights into algorithmic efficacy. Leveraging diverse datasets encompassing clinical, demographic, and laboratory parameters, we evaluate the performance of various algorithms including Support Vector Machines (SVM), Random Forest (RF), Gradient Boosting Machines (GBM), and Convolutional Neural Networks (CNN). Results indicate substantial variations in predictive accuracy, sensitivity, and specificity across different algorithms. SVM demonstrates robust performance with high accuracy and sensitivity, while RF and GBM exhibit competitive results albeit with nuances in predictive characteristics. Additionally, CNN, leveraging deep learning techniques, showcases promising potential, especially in handling complex data structures. Insightful analysis of feature importance highlights significant clinical parameters contributing to predictive models, offering valuable insights into disease mechanisms and risk factors. This study provides a comprehensive overview of machine learning methodologies for early CKD detection, facilitating informed decision-making in clinical practice and paving the way for personalized healthcare interventions aimed at combating the CKD epidemic. © 2024 IEEE.

关键词： Convolutional neural networks

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Efficient Communication in Wireless Sensor Networks Using Optimized Energy Efficient Engroove Leach Clustering Protocol

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Tsinghua Science and technology 2024年第4期29卷 985-1001页

作者： N.Meenakshi Sultan Ahmad A.V.Prabu J.Nageswara Rao Nashwan Adnan Othman Hikmat A.M.Abdeljaber R.Sekar Jabeen Nazeer SRM Institute of Science and Technology KattankulathurChennai 603203India Department of Computer Science College of Computer Engineering and SciencesPrince Sattam Bin Abdulaziz UniversityAlkharj 11942Saudi Arabia University Center for Research and Development(UCRD) Department of Computer Science and EngineeringChandigarh UniversityGharuanMohali 140413India Department of ECE Koneru Lakshmaiah Education FoundationVaddeswaramGuntur 522502India Department of Computer Science and Engineering Lakireddy Balireddy College of Engineering(A)NTR District 521230India Department of Computer Engineering College of EngineeringKnowledge UniversityErbil 44001Iraq Department of Computer Science Faculty of Information TechnologyApplied Science Private UniversityAmman 11937Jordan Department of Electronics and Communication Engineering(ECE) School of EngineeringPresidency UniversityBangalore 834001India

The Wireless Sensor Network(WSN)is a network that is constructed in regions that are inaccessible to human *** widespread deployment of wireless micro sensors will make it possible to conduct accurate environmental monitoring for a use in both civil and military *** make use of these data to monitor and keep track of the physical data of the surrounding environment in order to ensure the sustainability of the *** data have to be picked up by the sensor,and then sent to the sink node where they may be *** nodes of the WSNs are powered by batteries,therefore they eventually run out of *** energy restriction has an effect on the network life span and environmental *** objective of this study is to further improve the Engroove Leach(EL)protocol’s energy efficiency so that the network can operate for a very long time while consuming the least amount of *** lifespan of WSNs is being extended often using clustering and routing *** Meta Inspired Hawks Fragment Optimization(MIHFO)system,which is based on passive clustering,is used in this study to do *** cluster head is chosen based on the nodes’residual energy,distance to neighbors,distance to base station,node degree,and node *** on distance,residual energy,and node degree,an algorithm known as Heuristic Wing Antfly Optimization(HWAFO)selects the optimum path between the cluster head and Base Station(BS).They examine the number of nodes that are active,their energy consumption,and the number of data packets that the BS *** overall experimentation is carried out under the MATLAB *** the analysis,it has been discovered that the suggested approach yields noticeably superior outcomes in terms of throughput,packet delivery and drop ratio,and average energy consumption.

关键词： wireless sensor networks energy efficient engroove leach protocol meta inspired Hawks fragment optimization heuristic wing antfly optimization

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Smart Agriculture: Enhancing Crop Management through IoT-Based Real-Time Monitoring and Automation 9

Smart Agriculture: Enhancing Crop Management through IoT-Bas...

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9th International Conference on information technology and Digital Applications, ICITDA 2024

作者： Paliyanny, Hushalictmy Thinakaran, Rajermani Jalari, Somasekar Neerugatti, Vikram Nalluri, Madhusudana Rao Cholla, Ravindra Raman Inti International University Faculty of Data Science and Information Technology Malaysia Faculty of Engineering and technology Department of Computer Science and Engineering Bangalore India Icfai Foundation for Higher Education Department of Computer Science and Engineering Telangana Hyderabad India

ISBN: (纸本)9798331510732

Internet of Things plays an important role in agriculture in order to provide an innovative and smart solution to traditional farming. IOT is all about connecting physical devices to the internet and can access from a remote place. So that crop monitoring becomes easy even if the farmer is in a remote place. Due to automation, human efforts are required less. Also the IoT data helps the farmer make decisions about planting, harvesting or fertilizing. This paper explains the system we have implemented for monitoring the agriculture with the help of IoT. The system is developed in order to provide real time information on crop conditions to the farmer. For that, it uses various types of sensors connected to the microcontroller in order to measure parameters like temperature, soil moisture, and water level, and accordingly, it activates actuators like a fan, motor or pump to manage ventilation and irrigation. We have used the GSM module to send the alert to the farmer and the WiFi module is used to send the agriculture data to the cloud platform. So that farmer can monitor the farm or crops remotely. The system is continuously collecting farm data from the sensors. If the crop soil is very dry them system will turn on the water pump. Also, for high temperature, the fan will get activated in order to cool down that area. This feature is mostly used in indoor farming. The farmer can monitor the farm condition in real time with the help of cloud platform. This system is helpful for improving productivity, crop management, and save resources i.e. efficient use of water which prevents water wastage. © 2024 IEEE.

关键词： Soil moisture

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Methodology for Determining Proper Tyre Pressure 15

Methodology for Determining Proper Tyre Pressure

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

作者： Selvaraj, S.Kailasam Vineetha, V. Vinod, M. Vardhan, M.Vishnu Kumar, K.Vijay Reddy, N.Vinay Kumar Kalasalingam Academy of Research and Education Department of Information Technology Tamilnadu Virudhnagar India Kalasalingam Academy of Research and Education Department of Computer Science and Engineering Tamilnadu Virudhnagar India

ISBN: (纸本)9798350370249

This paper presents the design & implementation of a Tyre pressure monitor system (TPMS) utilizing a BMP280 sensor for automotive applications. The TPMS is composed of a transmitter unit installed in tyre and receiver unit mounted inside vehicle. The transmitter end consists of a BMP280 sensor, a microcontroller, and control buttons. It continuously measures the tyre pressure and temperature, transmitting the data wirelessly to the receiving unit. The receiver unit comprises a microcontroller, a display screen, LED indicators, and a buzzer for alerts. Upon receiving data from the transmitter, the receiver displays tyre pressure and temperature readings in display. Experimental results demonstrate the functionality, accuracy, and reliability of the proposed TPMS, providing timely alerts to drivers, thereby enhancing vehicle safety and performance. © 2024 IEEE.

关键词： Microcontrollers

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