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secured control systems through integrated IoT devices and control systems

Measurement: sensors 2022年 24卷

作者： Rajendran, N. singh, Rupinder Moudgil, Manu Raj Turukmane, Anil v. Umadevi, M. Glory, K.b. Department of Information Technology B.S.Abdur Rahman Crescent Institute of Science and Technology Chennai India Department of Computer Applications Chitkara University Rajpura140417 India Department of CSE Bhai Gurdas Institute of Engineering and Technology Punjab Sangrur148001 India Department of Computer Science & Engineering VIT- AP University Vijaywada Amaravati India Department of Computer Science Sri Adi Chunchanagiri Women's College Tamilnadu Cumbum India Department of Engineering English Koneru Lakshmaiah Education Foundation Andhra Pradesh State Vaddeswaram522302 India

The Internet of Things (IoT) provides customers with seamless integration and interoperability between gadgets, technologies, or products, and across multiple networks focus on control schemas. Regardless of the integration node, whether it is a smartphone, tablet, laptop, Tv, automobile, or other Internet-compatible gadget, end-users want a fast and clear connection. Intelligent IoT devices can act as flexible connectors with variable structures, allowing rapid responses and possibly universal access. Consequently, gadgets could pose serious security issues. When they are used to manage accounts receivable at IoT terminals, these methods are likely to replay, provoke human attacks in the middle, or by denial of service. Responding to the real-time challenge for safe control operations, this study provides a methodology that blends the characteristics of intelligent IoT systems with power system interfaces. A proposed method combines computation, cryptography, signal/image processing, and communication networks of authentication and authorization tasks, using both terminals and gateways. Unlike conventional methods, they provide a safer, more adaptable, and resilient setting for real-time performance. The researchers also describe Intel's IoT framework for power system inputs that are used in management, industrial, transport, and detailed monitoring contexts. © 2022 The Authors

关键词： Control systems

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smartcharge: Reducing Power Usage in Intelligent Homes

Smartcharge: Reducing Power Usage in Intelligent Homes

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Communication, Computing and Energy Efficient Technologies (I3CEET), International Conference on

作者： A. sonya Neha Chhabra Abhishek Kumar Gupta P. Lakshmi Harika b. Aishwarya R. Karthika Department of Information Technology B. S. Abdur Rahman Crescent Institute of Science and Technology Chennai Department of Information Technology Jagannath International Management School New Delhi India Department of Information Technology Jagannath International Management School New Delhi Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Bowrampet Hyderabad Telangana Department of Computer Science and Engineering(Cyber Security) Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Avadi India Department of EEE Theni Kammavar Sangam College of Technology Theni India

ISBN: (数字)9798331541583

ISBN: (纸本)9798331541590

smart homes oriented towards reminders and monitoring for healthcare must be reasoned, definitely centered on matured home or disability in a house. Accurately predicting the movements of people would be essential to offer such level support for residents in this situation. Our study helps to cater this requirement by proposing an improved forecasting model which is both accurate and has faster learning. For doing that, we here develop the recurrent version of an existing model; i.e., Extreme Learning Machine [11] to keep using temporal dependency of inputs into network in different time steps. This model is different since it can be constantly adapted to the needs of people in a smart home environment, especially those who anticipate needing medical support. so, finally this Recurrent Extreme Learning Machine model validates that for smart home technology evolutions as the time progresses continuously evolved intelligent are required to solve when it comes health care critical challenges. This type of learning model is not only apt in accuracy and speed which should be enough during the learning phase though; but it also allows machine to learn more about human behavior making them generally intelligent because now you have a pattern how humans behave. Advantages Of sexual Health These are certainly rewarding smart Home systems..

关键词： Training Adaptation models Accuracy Extreme learning machines smart homes Medical services Predictive models Telecommunication computing Forecasting Monitoring

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secure multicast group communications for IoT applications using WsN 3

Secure multicast group communications for IoT applications u...

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3rd International Conference on smart systems and Inventive Technology, ICssIT 2020

作者： Kartheeban, K. Hemalatha, J. Priya, C. venkatesulu, M. Kalasalingam Academy of Research and Education Department of Computer Science and Engineering Krishnankovil Tamilnadu India Sri Vidya College of Engineering and Technology Department of Computer Science and Engineering Virudhunagar Tamilnadu India V.S.V.N. Polytechnic College Department of Civil Engineering Virudhunaga Tamilnadu India Kalasalingam Academy of Research and Education Department of Information Technology Krishnankovil Tamilnadu India

ISBN: (纸本)9781728158211

Wireless sensor networks (WsNs) are one of the predominant backbone technologies for Internet of Things (IoTs). IoT enabled wireless sensor networks has sensor nodes and resource constrained sensor nodes are used for group communication in the form of multicasting for message deliveries rather than device-to-device communication. In order to secure the multicast group communication messages, it is essential to obtain an efficient key establishment and distribution scheme which maintains the integrity, and provides authenticity and confidentiality for the communication. This paper proposes two group key establishment and distribution protocols for secure multicast group communications between resource-constrained sensor nodes in IoT. Also, the relevance of the proposed protocols is examined based on three different performance parameters such as cost, overhead and packet loss using Ns3 simulations based on different nodes. © 2020 IEEE.

关键词： Internet of things

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An Innovative Method for Fraud Detection in E-Commerce Using DCNN-Multiclass svM Model

An Innovative Method for Fraud Detection in E-Commerce Using...

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Intelligent Algorithms for Computational Intelligence systems (IACIs), International Conference on

作者： Rafiya banu Abbarapu Ashok vijay Kumar Dwivedi Kotte Amaranadha Reddy Thulasimani T Neerav Nishant Department of Commerce B. S. Abdur Rahman Crescent Institute of Science and Technology Vandalur India Department of Mathematics School of Advanced Sciences VIT Andrapradesh India Department of Mathematics Vishwavidyalaya Engineering College Ambikapur (C.G.) India Department of Mathematics Kalasalingam Academy of Research and Education Krishnankoil India Department of Mathematics Bannari Amman Institute of Technology Sathyamangalam India Department of Computer Science and Engineering Babu Banarasi Das University Lucknow India

ISBN: (数字)9798350360660

ISBN: (纸本)9798350360677

A growing number of monetary transactions are taking place through different E-commerce platforms in this age of big data. Theft of personal information for fraudulent reasons is one of the risks that could arise from these opportunities. Credit cards are a prime target for cybercriminals seeking to steal personal data or execute fraudulent transactions due to their extensive use for online shopping. Intelligent fraud detection systems have come a long way, yet the system still haven't solved the notorious problems brought on by data imbalances. Following this important sequence will ensure that the data is properly prepared for feature extraction and model training. Prior to ATF processing raw log files pertaining to online e-commerce activity, a preprocessor removes data noise, such as click records created by customers who have not registered. Data mining for features an evolving metaheuristic algorithm called the multi-verse optimizer imitates the laws of the popular theory called multi-verse. While training the model, the proposed approach utilized DCNN-Multiclass svM. Two state-of-the-art approaches, CNN and svM, are beaten by the suggested approach. After using the strategy, accuracy improved by 95.65%.

关键词： Training support vector machines Accuracy Metaheuristics Feature extraction Data models Fraud Electronic commerce Data mining Convolutional neural networks

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A Novel IoT Framework and Device Architecture for Efficient smart city Implementation

A Novel IoT Framework and Device Architecture for Efficient ...

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International Conference on Trends in Electronics and Informatics (ICEI)

作者： sheetalrani R Kawale KDv Prasad D Palanikkumar P. Arockia Mary A.Yasmine begum Dankan Gowda v Department of Computer Science Karnataka State Akkamahadevi Women's University Vijayapura Karnataka India Symbiosis Institute of Business Management (SIBM) Symbiosis International (Deemed University) (SIU) Hyderabad Telangana India Department of CSE Dr NGP Institute of Technology Coimbatore Tamilnadu India Department of Information Technology V.S.B. Engineering College Karur Tamilnadu India Department of Electronics and Instrumentation Engneering Mohan Babu University (Erst while Sree Vidyanikethan Engineering College) Andhra Pradesh India Department of Electronics and Communication Engineering BMS Institute of Technology and Management Bangalore Karnataka India

Internet of Things (IoT) apps will provide fast and effortless management of smart cities. This networked service management is dependent on the Internet service Provider (IsP). This framework reduces physical human interaction in banking, railway counters, water and power counters, and so on. The IoT based applications makes the smart cities more sustainable. Recently, the Indian government is more focused and supportive towards building a reliable IoT based framework for developing innovative products, and businesses that rely mostly on cyberspace. This study discusses about the advantages, disadvantages and future possibilities of smart cities. The importance of the Internet of Things (IoT) in the development of India's smart cities, as well as how widespread adoption may swiftly disrupt people's everyday routines are also discussed.

关键词： smart cities Web and internet services Programming Market research Rail transportation Internet of Things security

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Identifying and Decomposing Compound Ingredients in Meal Plans Using Large Language Models

arXiv

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arXiv 2024年

作者： Kopitar, Leon bedrac, Leon strath, Larissa J. bian, Jiang stiglic, Gregor Faculty of Health Sciences University of Maribor Slovenia Faculty of Electrical Engineering and Computer Science University of Maribor Slovenia The Nu B.V. Leiden Netherlands University of Florida Florida United States Department of Health Outcomes and Biomedical Informatics College of Medicine University of Florida Florida United States Usher Institute University of Edinburgh Edinburgh United Kingdom

This study explores the effectiveness of Large Language Models in meal planning, focusing on their ability to identify and decompose compound ingredients. We evaluated three models—GPT-4o, Llama-3 (70b), and Mixtral (8x7b)—to assess their proficiency in recognizing and breaking down complex ingredient combinations. Preliminary results indicate that while Llama-3 (70b) and GPT-4o excels in accurate decomposition, all models encounter difficulties with identifying essential elements like seasonings and oils. Despite strong overall performance, variations in accuracy and completeness were observed across models. These findings underscore LLMs’ potential to enhance personalized nutrition but highlight the need for further refinement in ingredient decomposition. Future research should address these limitations to improve nutritional recommendations and health outcomes. © 2024, CC bY.

关键词： Food ingredients

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Hybrid RF-DNN-LsTM Ensemble for short-Term solar Energy Forecasting

Hybrid RF-DNN-LSTM Ensemble for Short-Term Solar Energy Fore...

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International Conference on Inventive Computation Technologies (ICICT)

作者： Indumathi v v T Ram Pavan Kumar M srilakshmi Ramya s Ch Harika Divya R sahith vellaturi Gopikrishna Department of CSE (AI&ML) B V Raju Institute of Technology Narsapur Medak Telangana India Department of Computer Science Kakaraparti Bhavanarayana College Vijayawada Andhra Pradesh India Department of CSE Koneru Lakshmaiah Education Foundation Vaddeswaram Andhra Pradesh India Department of AI&ML Aditya University Surampalem Dept of CSE CVR College of Engineering Hyderabad Dept of IT MLR Institute of Technology Hyderabad

ISBN: (数字)9798331512248

ISBN: (纸本)9798331512255

Given the drastic shift of global sentiment towards renewable energy, it becomes incredibly important to match supply with demand. However the highly variable nature of weather makes it difficult to accurately predict the output of a solar power plant. In this paper, solved this problem by using an ensemble model consisting of both machine learning and neural networks (NN) as base models to forecast the amount of energy that needs to be produced by a solar plant over a short-term time horizon, which in our case will be 0 minute (immediate), 5 minute, 30 minute and 90 minute. Each base model is fine tuned to encourage high diversity and low correlation to improve prediction accuracy. The expected stability or generalization from RF-DNN combined with the memory retention capability of the LsTM network should provide an ideal predictor for time series forecasting of a stochastic process like weather.

关键词： Renewable energy sources Time series analysis Weather forecasting stochastic processes solar energy Predictive models stability analysis Forecasting Long short term memory Random forests

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Deep Learning Model to Predict Pneumonia Disease based on Observed Patterns in Lung X-rays 4

Deep Learning Model to Predict Pneumonia Disease based on Ob...

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4th International Conference on Electronics, Communication and Aerospace Technology, ICECA 2020

作者： singh, Utkarsh Totla, Aditi Kumar, Dr. Prakash R. V. College of Engineering Dept. of Computer Science and Engineering Bengaluru Karnataka India B. I. T. Sindri Dhanbad Dept. of Production Engineering Jharkhand India

ISBN: (纸本)9781728163871

Due to the recent technological advancements, healthcare industry is also undergoing some major changes. The volume of medical data continues to increase at a staggering rate and artificial intelligence is poised to be the engine that drives improvements across the care continuum. Pneumonia is a serious disease, which can range from mild to life-threatening as it causes inflammation and fluid accumulation in the lungs and making it hard to breathe. It remains as the most serious for infants, children, and people older than 65 years. It is also considered to be a potential complication of the COvID-19 disease. Chest X-ray is an important pneumonia diagnosis worldwide. To help detect patients, who require intense medical care, artificial intelligence can be used to analyse lung imaging as a part of the clinical research study. The primary goal is to reduce the complex task of understanding scans through experts and provides easy prediction by developing a model that can predict the possibility of the disease in a patient through their lung X-ray images. © 2020 IEEE.

关键词： Convolutional neural networks

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secure Cloud Data storage system Using Hybrid Paillier–blowsh Algorith

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computers, Materials & Continua 2021年第4期67卷 779-798页

作者： bijeta seth surjeet Dalal Dac-Nhuong Le vivek Jaglan Neeraj Dahiya Akshat Agrawal Mayank Mohan sharma Deo Prakash K.D.verma SRM University Delhi-NCRSonipatHaryana131029India Institute of Research and Development Duy Tan UniversityDanang550000Vietnam Department of Information Technology Duy Tan UniversityDanang 550000Vietnam Graphic Era Hill University DehradunUttarakhand248171India Amity University Haryana122412India Principal Software Lead Zillow Inc.USA School of Computer Science&Engineering Faculty of EngineeringSMVD UniversityKakryalKatra182320India Material Science Research Laboratory Department of PhysicsS.V.CollegeAligarh202001India

Cloud computing utilizes enormous clusters of serviceable and manageable resources that can be virtually and dynamically recongured in order to deliver optimum resource utilization by exploiting the pay-per-use ***,concerns around security have been an impediment in the extensive adoption of the cloud computing *** this regard,advancements in cryptography,accelerated by the wide usage of the internet worldwide,has emerged as a key area in addressing some of these security *** this document,a hybrid cryptographic protocol deploying blowsh and Paillier encryption algorithms has been presented and its strength compared with the existing hybrid Advanced Encryption standard(AEs)and Rivest shamir Adleman(RsA)*** for secure data storage protocol in two phases have been *** proposed hybrid protocol endeavors to improve the power of cloud storage through a decrease in computation time and ciphertext *** have been carried out with Oracle virtual box and Fog server used on an Ubuntu 16.04 *** grouping of asymmetric and homomorphic procedures has demonstrated enhanced *** usage has helped in decreasing the storage space and computation *** analysis in terms of computation overhead and quality of service parameters like loads of parameters with and without attacks,throughput,and stream length for different modes of block cipher mode has been carried *** analysis has been carried out by utilizing the Hardening Index as an audit parameter using Lynis ***,for halting the aforementioned approaches and for regulating trafc,rewall protection has been generated in the chosen hybrid ***,enhancements in the performance of the Paillier and blowsh hybrid scheme with and without compression compared to the existing schemes using RsA and AEs procedures have been demonstrated.

关键词： Cryptography blowsh homomorphic paillier cloud computing

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Local Directional Maximum Edge Patterns for facial expression recognition

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Journal of Ambient Intelligence and Humanized Computing 2020年第5期12卷 4775-4783页

作者： Maheswari, v. Uma varaprasad, G. Raju, s. viswanadha Department of Computer Science and Engineering Vardhaman College of Engineering Hyderabad India Department of Computer Science and Engineering B.M.S. College of Engineering Bangaluru India Department of Computer Science and Engineering JNTUHCEJ Jagityal India

Cognitive science and neuroscience use human facial expressions of emotion. Every single facial expression can be seen at different passions in a face space. Nowadays, facial expression recognition and analysis is vital due to the demand of introducing advanced biometric applications in every domain space. The imperative task in facial expressions of emotion classification is precise feature extraction, which helps to get detailed description of facial marks. Existing feature descriptors are suffering from various problems such as intensity variations, discrimination, vulnerability etc. In this paper, propose a new feature descriptor method called LDMEP (Local Directional Maximum Edge Patterns) for facial expression analysis to overcome the hindrance. We calculated the gradients in four directions of reference pixel to elicit the more feature for better recognition instead of calculating the local differences among neighboring pixels. We also access the orientations of the pixels then thresholded based on the dynamic threshold to avoid the featureless area calculation. Furthermore, we considered only dominant magnitude and orientation directions instead of all eight directions to generate feature. Thus, imperative and efficient features are covered in dominant positions to detect the strong edges. The paper confers that how the subsequent model can be used for the recognition of facial expression of emotion.

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