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2023 International Conference on Intelligent Systems for Communication, IoT and Security, ICISCoIS 2023

作者： Pyingkodi, M. Blessing, N R Wilfred Thenmozhi, K. Kanimozhi, K. Pavithira, L. Balakrishnan, P K Sandhya Kongu Engineering College Department of Computer Applications Erode India IT Department Oman Kristu Jayanti College Department of Computer Science Bengaluru India Sri Krishna Adithya College of Arts and Science Department of Information Technology Coimbatore India PSG College of Arts and Science College Department of Computer Science with Data Analytics Coimbatore India CMS College of Science and Commerce Department of Computer Science Coimbatore India

ISBN: (纸本)9798350335835

On international basis, one in all our century's essential problems is air pollutants. Every year, the range of individuals killed via way of means of pollutants climbs, with air pollutants being the main cause. The Internet of Things (IoT) is used worldwide community of 'inventive gadgets' which could sense, and join to, and have interaction with different humans and systems. The solution is furnished via way of means of a sensor-primarily based totally hardware module that can be deployed anywhere. These gadgets, which can be hooked up on mild poles, homes, and different structures, wirelessly speak air pleasant facts to a far flung server. The microprocessor collects facts from IoT sensing gadgets and sends it to a cloud server for processing. These facts can be used to expecting while the vicinity is maximum polluted. The machine additionally gives air pleasant statistics through a cell application, permitting clients to choose alternatives wherein the air pleasant is great. It is likewise a totally less expensive device. © 2023 IEEE.

关键词： Internet of things

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Formula One: Racing Intelligence in the Age of AI

Formula One: Racing Intelligence in the Age of AI

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Advancements in Electrical, Electronics, Communication, Computing and Automation (ICAECA), International Conference on

作者： R.K. Kavitha S. Kanishk Department of Computer Applications Kumaraguru College of Technology Coimbatore Department of Data Science Kumaraguru College of Liberals Arts and Science Coimbatore

ISBN: (数字)9798331525439

ISBN: (纸本)9798331525446

This study proposes an all-inclusive, data-driven system to optimize the performance of Formula One racing by incorporating state-of-the-art technology such as blockchain, edge computing, artificial intelligence, and real-time telemetry. The current systems of Formula 1 depend heavily on manual analysis and traditional data collection, thereby limiting the ability to make decisions in real time. The proposed system, however, incorporates high-resolution telemetry sensors with Nvidia Jetson edge computing devices, AI-driven prediction models, and secure blockchain protocols so that performance improvement during a race can be achieved even faster and more accurately. The key components are in the form of telemetry sensors for real-time data acquisition, edge computing for real-time processing, blockchain technology for secure data exchange and artificial intelligence models for predictive analytics. This method improves vehicle performance, adjusts to the race condition changes, and enhances general decision-making with an efficient, transparent, and scalable solution. The system architecture also accommodates future technological development as well as cooperation within the motorsport industry. The implications of the findings spread wider to driverless car industries and other data-dependent sectors, showing how blockchain and AI can change the game plan of racing and corporate formations.

关键词： Computational modeling Predictive models Real-time systems Sensor systems data models Blockchains Telemetry Artificial intelligence Intelligent sensors Edge computing

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An Efficient Multilevel Threshold Image Segmentation Approach Based on Rafflesia Optimization Algorithm 1

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18th International Conference on Intelligent Information Hiding and Multimedia Signal Processing, IIH-MSP 2022

作者： Zhou, Jin-Liang Chu, Shu-Chuan Weng, Shaowei Zheng, Wei-Min College of Computer Science and Engineering Shandong University of Science and Technology Qingdao China College of Science and Engineering Flinders University Adelaide Australia Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology Fuzhou China

ISBN: (数字)9789819901050

ISBN: (纸本)9789819901043

Segmentation is an important step from image processing to analysis. The thresholding technique based on entropy has become one of the most commonly used techniques in this field. Among the many thresholding techniques based on entropy, the minimum cross-entropy threshold (MCET) has been dramatically developed. The key to multilevel MCET technology is quickly finding the optimal threshold point. MCET is a relatively time-consuming technique, so we use optimization techniques to search for optimal multilevel thresholds. Rafflesia optimization algorithm (ROA) is an optimization algorithm inspired by rafflesia growth theory to solve real-world multi-parameter optimization problems. In this paper, Rafflesia optimization algorithm, particle swarm optimization, gray wolf optimization, whale optimization algorithm, and multi-version optimizer are used to segment six images and the peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), and feature similarity index (FSIM) as evaluation metrics. The experimental results show that the ROA algorithm can achieve higher-quality image segmentation. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Image segmentation

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Wireless Body Area Network (WBAN) Centric Healthcare System for Continuous Monitoring 9

Wireless Body Area Network (WBAN) Centric Healthcare System ...

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9th International Conference on Advanced Computing and Communication Systems, ICACCS 2023

作者： Kumar, M. Anand Valli Mayil, V. Jeyalakshmi, T. Ratha Ghanshala, Anshul Gupta, Anshika Graphic Era University Dept of Computer Applications Dehradun India Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Andhra Pradesh India Jain University School of Sciences Department of Data Science Banglore India The Chinese University of Hong Kong Department of Computer Science and Engineering Hong Kong Hong Kong

ISBN: (纸本)9798350397376

Stroke is one the most dangerous disease all across the world which would lead to serious disability or death among all aged people due to changes in environmental factors. Recent reports indicate that a mere 75 million people will suffer from stroke-related disabilities by the year 2030. It was also identified that a stroke occurs every 40 seconds on average across the globe. One out of every five stroke-related patients would die in a year. A Recent health survey indicates that most developed countries face a high number of stroke patients in very recent years. There is also a rapid growth in the number of stroke cases in developing countries. To prevent the high-risk factors related to stroke diseases, there is much need for continuous observation of physiological signals in real-time and to analyze the data. One of the main strategies is wearable technology that employs wireless sensor networks to link numerous medical devices and associated systems. They have to ability to function independently across several platforms. Wearable devices are used in most medical applications because of their noteworthy qualities, including adaptability, versatility, inexpensiveness, and efficiency in various platforms. Integration of the Internet of Things (IoT) with wearable devices and Fog Computing provides a more flexible mechanism to deploy a continuous healthcare monitoring system to monitor vital symptoms and to detect the early phase of stroke factors instantly. To predict stroke-related disorders, this research proposes a Wireless Body Area Network (WBAN)-centric healthcare system for continuous monitoring and examination of physiological signals. Initially, the proposed model predicts the nature of the symptoms based on the parameters collected by the IoT sensors. Deep learning techniques are then applied to predict the risk factors that are critical to the stroke patient during the emergency condition. The key contribution of this research work is to provide an emerg

关键词： Internet of things

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Estimating fish weight growth in aquaponic farming through machine learning techniques 3

Estimating fish weight growth in aquaponic farming through m...

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3rd IEEE International Conference on Intelligent Technologies, CONIT 2023

作者： Kumar, Purushottam Tiwari, Pranav Srinivasulu Reddy, U. Machine Learning & Data Analytics Lab CoE in Artificial Intelligence National Institute of Technology Trichy India Indian Institute of Information Technology Tiruchirappalli Computer Science and Engineering Trichy India National Institute of Technology Machine Learning & Data Analytics Lab Department of Computer Applications Trichy India

ISBN: (纸本)9798350338607

Due to the ever-growing population, rapid urbanization, unusual environmental change, and dwindling water supply, the food production from conventional farming techniques won't be able to keep up with increasing food demand for long, leading to severe food scarcity. Aquaponics is emerging as a viable alternative to food cultivation techniques but lacks efficiency, hindering its adoption on a large-scale production basis. The adoption of Artificial Intelligence (AI) techniques in smart aquaponic farming can further contribute to increasing the yield in aquaponics. In the study, both linear and non-linear regression algorithms such as linear regression, lasso regression, ridge regression, KNN regression, SVR, and decision tree regression were trained on aquaponic fish pond dataset, evaluated on the basis of multiple metrics like MAE, MSE, RMSE, and RMSLE, and a competitive analysis was done to determine the best algorithm for predicting the harvest weight of the fish in aquaponic ponds. The results showed that KNN regression and decision tree regression algorithms have lowest error and highest accuracy of 99.15% and 99.1% respectively, for predicting the harvest weight of the fish. The results obtained could be scaled up to commercial levels and used in regulating nutrient concentration and aquatic environmental conditions © 2023 IEEE.

关键词： Fish

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An Effective Training Strategy for Enhanced Source Camera Device Identification 26th

An Effective Training Strategy for Enhanced Source Camera De...

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26th International Conference on Pattern Recognition, ICPR 2022

作者： Manisha Li, Chang-Tsun Kotegar, Karunakar A. Department of Data Science and Computer Applications Manipal Institute of Technology Manipal Academy of Higher Education Karnataka Manipal576104 India School of Information Technology Deakin University Geelong3216 Australia

ISBN: (纸本)9783031377440

Source camera identification is a forensic problem of linking an image in question to the camera used to capture it. This could be a useful tool in forensic applications to identify potential suspects of cyber-crime. Over the last decade, several successful attempts have been made to identify the source camera using deep learning. However, existing techniques that provide effective solutions for camera model identification fail to distinguish between different devices of the same model. This is because cameras of different brands and models were used to train the data-driven system when dealing with exact device identification. We show that training the data-driven system on different camera models opens side-channel information on model-specific features, which acts as interference for identifying individual devices of the same model. Thus, we provide an effective training strategy that involves a way to construct the dataset for enhanced source camera device identification. The experimental results suggest that involving only cameras of the same model for training improves the discriminative capability of the data-driven system by eliminating the threat of interfering model-specific features. © Springer Nature Switzerland AG 2023.

关键词： Convolutional neural networks

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Short-term power load forecasting based on BP neural network optimized by genetic algorithm 7

Short-term power load forecasting based on BP neural network...

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2022 7th International Conference on Electronic Technology and Information science, ICETIS 2022

作者： Xiong, Neng Liang, Quan Xiong, Neng Yu, Wenze Hong, Chuanbo Wang, Hansong School of Computer Science and Mathematics Fujian University of Technology Fujian Provincial Key Laboratory of Big Data Mining and Applications Fuzhou China

ISBN: (纸本)9783800757794

Accurate short-term power load forecasting is of great significance to the safe operation of power systems. Aiming at the defects of traditional BP neural network that it is easy to fall into local optimization and low prediction accuracy, Proposed to optimize the BP neural network with genetic algorithm, To improve the initial weights and thresholds of the network, increase the convergence speed, And then establish the genetic algorithm to optimize the prediction model of the BP neural network. The model uses one input, output layer, double hidden layers, Analyze and forecast the power load data of a company. Experiment simulation through matlab platform, The results show that the BP neural network model optimized by genetic algorithm has higher prediction accuracy, better stability, and more practical value. © VDE VERLAG GMBH ∙ Berlin ∙ Offenbach.

关键词： Genetic algorithms

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Secure Online Banking Authentication System Using Time Bound Password

Secure Online Banking Authentication System Using Time Bound...

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Computing, Power and Communication Technologies (IC2PCT), IEEE International Conference on

作者： Ronson Danthy K Pratham Pai Vidya Rao Department of Data Science and Computer Applications Manipal Institute of Technology Manipal Academy of Higher Education Manipal India Department of Computer Science and Computer Applications Manipal Institute of Technology Manipal Academy of Higher Education Manipal India

The banking system is a key component of financial transactions and economic expansion in the modern world. The introduction of internet banking, however, has created some brand-new difficulties, particularly in providing safe user authentication. This study investigates the flaws in conventional authentication techniques, including passwords and SMS-based One-Time Passwords (OTP), and suggests a creative fix. By using Time-Based One-Time Password (TOTP) created by an authenticator app and encrypting the user credentials with this as key, the proposed solution improves security. By addressing problems including poor password habits, SMS theft, OTP delivery delays, phishing, and social engineering attacks, this method strengthens the security of online banking. The approach provides a thorough framework for building this secure authentication system by detailing TOTP generation and encryption. This system successfully defends against popular attacks such as replay attacks, brute force attacks, rainbow tables, packet sniffing, and random guessing.

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Two-stage charging scheduling optimization of electric vehicles based on user behavior 4

Two-stage charging scheduling optimization of electric vehic...

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2022 4th International Conference on Electronic Engineering and Informatics, EEI 2022

作者： Hong, Chuanbo Liang, Quan Liang, Qiaoxin Fujian Provincial Key Laboratory of Big Data Mining and Applications College of Computer Science and Mathematics Fujian University of Technology Fuzhou China

ISBN: (纸本)9783800759231

With the continuous improvement of the penetration rate of electric vehicles, it hurts the economic and stable operation of the power grid, to reduce the power grid load fluctuation caused by random charging of electric vehicles, In this paper, a two-stage charging strategy is proposed. Firstly, the probability distribution model is calculated according to the travel characteristics of users, and then the charging model is established by the Monte Carlo method combined with the information on charging piles, battery characteristics, and automobile scale. Finally, the impact of this strategy on the power grid is described by taking valley peak value, saving rate, and travel rate as evaluation indexes. Simulation results show that this strategy can meet the driving needs of users, reduce travel anxiety information, and reduce the peak-valley load gap of the power grid. It is an orderly charging strategy integrating the power grid and user sides. © VDE VERLAG GMBH ∙ Berlin ∙ Offenbach.

关键词： Electric loads

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ENERGY-EFFICIENT ANT ROUTING ALGORITHM FOR OPTIMIZED PATH SELECTION NETWORK LONGEVITY IN WIRELESS SENSOR NETWORKS

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Telecommunications and Radio Engineering (English translation of Elektrosvyaz and Radiotekhnika) 2025年第2期84卷 21-42页

作者： Zelani, Shaik Khader Babu, D. Nagabhushana Surendra, Doddaga Rahman, Mohammad Zia Ur Department of Electronics and Communication Engineering Sri Vasavi Institute of Engineering and Technology-Autonomous Pedana India Department of Data Science Malla Reddy University Telangana Hyderabad India Department of Computer Science and Applications Koneru Lakshmaiah Education Foundation K L Deemed to be University Vaddeswaram Andhra Pradesh India Department of Electronics and Communication Engineering Koneru Lakshmaiah Education Foundation K L Deemed to be University Vaddeswaram Andhra Pradesh India

The exponential growth of wireless sensor networks (WSNs) in various applications underscores the critical need for energy-efficient routing protocols to extend network lifespan. This paper introduces the Energy-Efficient Ant Routing Algorithm, a novel bioinspired routing strategy derived from ant colony optimization (ACO), which integrates traditional pheromone mechanisms with dynamic energy-aware adaptations to prioritize routes through nodes with higher residual energy levels, thereby balancing load and prolonging network functionality. The model’s adaptive pheromone updating mechanism, which considers both the cumulative path energy cost and the minimum residual energy of nodes along the path, encourages the use of energy-efficient routes and mitigates rapid energy depletion in critical network areas. This approach not only extends operational network lifetime but also supports higher data reliability and throughput in challenging WSN scenarios. The results of the proposed model demonstrated significant improvements over traditional ACO and other common energy-aware routing protocols. The network lifespan is extended by ~ 25% compared to standard ACO and by 15% compared to the best-performing conventional energy-aware routing protocol under the same conditions. Furthermore, EEARA achieved a 20% reduction in average energy consumption per node and enhanced the data delivery ratio by 10% due to more stable routing paths. The results underline the model’s potential as a scalable, efficient routing solution for energy-limited networks, promising substantial improvements in sustainable network operations. © 2025 by Begell House, Inc.

关键词： Ant colony optimization

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