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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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Autonomous Vehicle Traffic Flow Optimization Using AI and 5G Communication Networks

Autonomous Vehicle Traffic Flow Optimization Using AI and 5G...

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Device Intelligence, Computing and Communication technologies, (DICCT), International Conference on

作者： Jayant Shekhar Rama Prabha K. P S. Prashanth Umamaheswari K Rahul S G K. Gayathri Department of CSE Sharda School of Engineering and Technology Sharda University Greater Noida UP India School of Computer Science and Engineering Vellore Institute of Technology Chennai Tamil Nadu India Department of Aeronautical Engineering Excel Engineering College Namakkal Tamil Nadu India Department of Information Technology V.S.B. Engineering College Karur Tamilnadu India Department of Electronics and Communication Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Tamil Nadu India Department of Artificial Intelligence and Data Science St. Joseph's Institute of Technology Chennai Tamil Nadu India

ISBN: (数字)9798331543358

ISBN: (纸本)9798331543365

Real-time optimization of autonomous vehicle (AV) traffic flow using 5G communication networks and AI algorithms could overcome the limitations of static traffic enforcement, which uses outdated technologies to manage fluctuating traffic demands. It allows dynamic flow. Such commodities cannot adapt in real time because to their rigorous handling requirements, which disrupts traffic dynamics and creates construction delays and traffic congestion. The study suggests transmitting real-time vehicle status data to nearest intelligent crossings via vehicular wireless networks (VWNs). These intersections enhance traffic flow and reduce 5G network congestion using AI algorithms. Predictive analytics will help AVs change routes while on the road to maximize travel efficiency. It finds that peak congestion, average route duration, and fuel use reduce vehicle CO2 emissions by 66.7%, 22%, and 15%, respectively. It determined that the proposed system was a viable design route for a deployable smart city transportation solution because it outperformed traditional techniques in economic and environmental criteria.

关键词： 5G mobile communication Smart cities Heuristic algorithms Real-time systems Artificial intelligence Vehicle dynamics Autonomous vehicles Optimization Traffic congestion Intelligent sensors

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Autonomous Vehicle Traffic Flow Optimization Using AI and 5G Communication Networks 3

Autonomous Vehicle Traffic Flow Optimization Using AI and 5G...

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3rd IEEE International Conference on Device Intelligence, Computing and Communication technologies, DICCT 2025

作者： Shekhar, Jayant Rama Prabha, K.P. Prashanth, S. Umamaheswari, K. Rahul, S.G. Gayathri, K. Sharda School of Engineering and Technology Sharda University Department of Cse UP Greater Noida India School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Chennai India Excel Engineering College Department of Aeronautical Engineering Tamil Nadu Namakkal India V.S.B. Engineering College Department of Information Technology Tamilnadu Karur India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Electronics and Communication Engineering Tamil Nadu Chennai India St. Joseph's Institute of Technology Department of Artificial Intelligence and Data Science Tamil Nadu Chennai India

ISBN: (纸本)9798331543358

关键词： Traffic congestion

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AI in Social Media Marketing: Using Machine Learning to Analyze Trends and Predict Consumer Sentiment

AI in Social Media Marketing: Using Machine Learning to Anal...

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Pervasive Computational technologies (ICPCT), International Conference on

作者： bhavana Jamalpur Somanchi Hari Krishna Romica bhat Sneh binod Prasad Sourabh bhattacharya Dhiraj Kapila School of CS & AI SR University Warangal Telangana India Department of Business Management Vignana Bharathi Institute of Technology Telangana State India Journalism and Mass Communication Lovely Professional University B. Tech (CSE) SRM Institute of Science and Technology Chennai India Department of Management & Social Science Haldia Institute of Technology Haldia West Bengal Department of Computer Science & Engineering Lovely Professional University Phagwara Punjab India

ISBN: (数字)9798331508685

ISBN: (纸本)9798331519476

Social media marketing (SMM) is prominent in the society at present. Individuals use social networking sites to buy different items. The rapid rise of social networking sites has rendered them essential tools for evaluating consumer sentiments and forecasting a wide range of societal and economic patterns. This study gathered statistics from a variety of social media channels. This study examined data to anticipate user activity on social networking sites. This study took into account user statistics from Twitter, LinkedIn, YouTube, Facebook, Instagram, and Pinterest, among other platforms. because social media networks provide a wide range of statistics at elevated speeds and volumes, predicted big data techniques were applied in this study. This investigation studied user conduct on social networking sites using specific metrics and variables. This study examined user perception and attitudes towards social networking platforms. This study preprocessed data to remove outliers, disturbances, errors, and duplicate records, resulting in high-quality results. As a result, in this paper, an integrated system combining sentiment evaluation and machine learning (ML) methods is constructed. This study utilized computational models and ML to anticipate user conduct on social networking platforms. The framework predicts user conduct on social networking platforms. Eighty percent of data has been employed for training, with twenty percent for testing. After testing many conventional and ensemble ML classifiers, decision trees outscored every other method.

关键词： Training Measurement Sentiment analysis Video on demand Social networking (online) Machine learning Market research Web sites Multimedia communication Testing

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5G Tiny-ML AI-based iot eNose system for Hazardous Odour Detection and Classification

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IEEE Sensors Journal 2025年

作者： Fayos-Jordan, Rafael Alselek, Mohammad Khadmaoui-bichouna, Mohamed Segura-Garcia, Jaume Alcaraz-Calero, Jose M. University of the West of Scotland School of Computing Engineering and Physics Scotland PA1 2BE United Kingdom Tech Lead London United Kingdom ETSE Universitat de Valencia Valencia46100 Spain Aston University School of Computer Science and Digital Technologies BirminghamB4 7ET United Kingdom

More than 2 million people have died in the world in 2019 exposed to hazard substances. In this context, it is of paramount importance to deliver effective systems to help minimizing such number of dead. The usage of advanced technologies, such as artificial intelligence (AI) and the Internet of Things (iot), plays a critical role in the detection of hazardous substances within Industry 4.0. The combination of these technologies enhance the efficiency and accuracy of monitoring harmful materials, improving safety standards and operational processes in industrial environments. AI can be used to analyse vast amounts of data for identifying patterns and predicting potential hazards, while iot connects various devices and sensors to ensure real-time tracking and prompt responses to risks. This technological synergy is essential for modern industries aiming to create safer and more automated systems. In this work, we propose a 5G AI-iot e-Nose system for the real-time detection and classification of 5 hazardous odours. The proposed AI model is very lightweight and it is affordable for our iot micro controller unit. The system has been validated in laboratory conditions, but has the advantage and potential impact to be effective in any scenario. © 2001-2012 IEEE.

关键词： Industrial poisons

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Cloud-based fault prediction using iot in office automation for improvisation of the health of employees

Cloud-based fault prediction using IoT in office automation ...

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Advance Computing and Innovative technologies in engineering (ICACITE), International Conference on

作者： Nakul Shahdadpuri Somanchi Hari Krishna SathiJyothirmaye Reddy C. Ganesh Kirti Agarwal Sourabh bhattacharya IPG Programme {(B.Tech + M.Tech (It)} IIITM Gwalior Department of Business Management Vignana Bharathi Institute of Technology Malkajigiri Telangana Department of Management Marwadi University Rajkot Gujarat Computer and Communication Engineering Sri Eshwar College of Engineering Anna University Chennai ITERC College Delhi (NCR) India Department of Management and Social Science Haldia Institute of Technology West Bengal

Modern technology is the best thing to be used in every commercial sector. The devices are well equipped to understand the problems of the users. The offices are now been maintained by technology the devices for daily monitoring even the electrical appliances are been monitored and maintained by technology. In previous times the best method of this work is to make it via cloud computing, but now as technology has evolved and evolved new adding devices are making system malfunction. The fault of cloud computing needs to be identified, So the best method is to automate the office with the introduction of iot. it is a platform equipped with all the facilities for monitoring and maintaining the devices inside the office, even the working capacity of employees is also measured via this method. The study is showing the effectiveness of the iot to identify the faults of cloud computing related to maintaining the working method of the office. The data collected for this study is secondary data like articles, books, journals and many more. Then gathered data is analyzed by qualitative data analysis. The results are quite positive about the use of it to automate the office and its working procedure.

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Face Spoof Detection Using Gray Level Co-Occurrence Matrix and Discrete Wavelet Transform Feature Extractor

Face Spoof Detection Using Gray Level Co-Occurrence Matrix a...

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International Conference on Computing sciences (ICCS)

作者： Amal Mathew Kaushik Daiv Polkumpally Rohan Goud Piyush Talreja Sai Sanjana Reddy Vatte B.tech Computer Science Ms Ramaiah University of Applied Sciences Bangalore Karnataka India B.E. Computer Science MIT College of Engineering Pune Maharashtra India B.E Computer Science Chaitanya Bharathi Institute of Technology Hyderabad India B.E Computer Science Maharashtra Institute of Technology Pune Maharashtra India

ISBN: (纸本)9781665494465

Face identification using ML (machine learning) is well-known. Attendance structures may benefit from this method. Using this method, you may achieve the desired area, as well as beneficial attributes and a dataset, by preparing two sets of data again for test and training phases. To distinguish between a testing set and a test sets, a photograph is used as a testing set. An ensemble classification method is used to sort the test images into categories like “identified” and “unidentified.” This model can't provide reliable findings since it simply divides data into two categories. The development of GLCM was motivated by the need to use texture properties to identify faces. The existence of the query picture is noted once face detection has taken place. In simulation findings, the new model outperforms the baseline models in terms of accuracy. Keywords—Ensemble classifier, GLCM, Face Spoof, SVM, DWT

关键词： Training Image segmentation Face recognition Training data Machine learning Transforms Feature extraction

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Effectiveness of Machine Learning Algorithms in Forecasting AQI

Effectiveness of Machine Learning Algorithms in Forecasting ...

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technological Advancements and Innovations (ICTAI), International Conference on

作者： Sudhanshu Pal Debanshi Pramanik Ena Jain B. Tech Computer Science & Engineering DIT University Dehradun India School of Computing DIT University Dehradun India

ISBN: (纸本)9781665420884

Air pollution has become major cause of global concern in recent times. Global warming, climate changes and many major hazardous diseases are all reasons of air pollution. Rapid industrialization, urbanization, deforestation have all accelerated the air pollution level. Air is getting polluted due to release of poisonous gases by industrialization, vehicular emissions, unplanned construction activities. Everyone is at an alarming risk whether they are children or elder. Among metro cities of the world Delhi is known as one polluted metro cities in the world and we have used AQI data of Delhi city as our testbed. This work proposes a model in which Machine Learning algorithms are applied to measure the future AQI data from the current and past available AQI data. We have used various algorithms like Linear Regression, Lasso Regression, Random Forest and Decision Tree to predict the future AQI with past AQI and found that Decision Tree algorithm gives best prediction with limited number of data entries and Random Forest gives best result with larger dataset.

关键词： technological innovation Machine learning algorithms Urban areas Prediction algorithms Air pollution Pollution measurement Planning

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An ML-based approach to transform stimuli into characteristic oscillatory activity of the beta band

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AIP Conference Proceedings 2023年第1期2946卷

作者： Sunny bhatt Aditi Jindal Department of Computer Engineering (B.E. Computer Engineering) Gujarat Technological University Ahmedabad-382424 Gujarat India Department of Computer Science and Engineering (B. Tech. Computer Science) Gurugram University Gurugram-122003 Haryana India

brain oscillations are essential to math and spatial skills. Cognition, attention, memory processing and language are influenced by these oscillations. Neurological explanations proposed to account for the fluctuations in brain activity help us understand how important the relation of rhythmicity is within a neural network. Desynchronization refers to the sharp and irregular changes of the oscillations in brain waves called rapid-eye movement sleep spindle activity during sleep. It is the disruption of interactions between resting oscillations of brain regions typically producing rhythmic neuronal firing that occur simultaneously in different frequency bands like alpha or beta. The beta band is associated with attention and self-focused activities like perceiving, thinking, judging, and problem solving. In this paper, the authors develop a neural model of nonlinear oscillators that transforms stimuli into characteristic oscillatory activity of the beta band.

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Research on varietal classification and germination evaluation system for rice seed using hand-held devices

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Acta Agriculturae Scandinavica, Section b — Soil & Plant science 2021年第9期71卷 939-955页

作者： S. Durai C. Mahesh a Department of Computer Science and Engineering School of Computing Vel Tech Rangarajan Dr Sagunthala R & D Institute of Science and Technology Chennai India b Department of Information Technology Vel Tech Rangarajan Dr Sagunthala R & D Institute of Science and Technology Chennai India

AbSTRACTFormulae display:?Mathematical formulae have been encoded as MathML and are displayed in this HTML version using MathJax in order to improve their display. Uncheck the box to turn MathJax off. This feature requires Javascript. Click on a formula to *** Seed varietal classification and germination evaluation system is developed to identify the variety and evaluate the germination of rice seeds using Digital Image processing system. For economic and ease of usage, we have used mobile phones to take digital images. The objective of our research is, it will be easily used by farmers. Our research is done on four major rice varieties, which commonly cultivated by Tamilnadu farmers, namely (1) Andhra Ponni (2) Atchaya Ponni (3) KO50 and (4) IR 20 was collected from Tamilnadu Agricultural University Tiruchirappalli, Tamilnadu, India. We have extracted 24 features: 3 colour features, 13 morphological features and 8 textural features. Created data set tested with all possible classification algorithms, out of which Ensemble classification algorithm gives 91.6% accuracy for Variety Identification and SVM gives 63% of accuracy for germination prediction. According to the germination percentage, a support vector machine (SVM) was utilised to categorise the seeds specimens into 3 groups: healthy, old, and deceased. The categorisation prediction accuracy has always been significant. We have created the data set for successful identification of varieties and germination prediction for the above-mentioned varieties; it is publicly available for usage.

关键词： Rice seed varietal classification seed quality seed germination germination prediction

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