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IoT Based Control Networks and Intelligent Systems (ICICNIS), International Conference on

作者： Vijayta Patil Jhankar Moolchandani Ashutosh Kumar Dubey Department of Computer Science & Engineering Amity School of Engineering and Technology Amity University Gwalior Madhaya Pradesh India Chitkara University School of Engineering and Technology Chitkara University Himachal Pradesh India

ISBN: (数字)9798331518097

ISBN: (纸本)9798331518103

In today's world, the growth of connected networks has also created a significant risk due to increasing risks of cyber-attacks. In this paper, various hybrid cybersecurity frameworks have been reviewed and analyzed that utilize machine learning approaches in combination with federated learning. These frameworks are assessed in terms of their security against complex cyber threats. In particular, machine learning focuses on improving threats detection by searching for patterns and other oddities in complex datasets, while the federated learning decentralizes the topology of the model training. This decentralization helps to enhance strict data compliance requirements and reduce data exposure risks. When synergized in the dual approach, the security system is strengthened as well as the enforcement of data privacy and other compliance where available. Empirical results have proved that this methodological approach provides better outcomes especially aimed at detection among other benchmarks. The improvement due to the hybridization indicates a scalable and robust solution that can adapt the evolving cyber threats landscape. The hybrid combination may establish new cybersecurity standards and open the door to more secure digital interactions across various sectors.

关键词： Training Data privacy Federated learning Reviews Threat assessment Topology Sensors Safety Reliability Standards

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Human Pose Estimation using Deep Learning Techniques 22

Human Pose Estimation using Deep Learning Techniques

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Proceedings of the 4th International Conference on Information Management & Machine Intelligence

作者： Anant Grover Deepak Arora Anuj Grover Department of Computer Science and Engineering Amity School of Engineering and Technology Amity University Uttar Pradesh Lucknow Campus. India Department of Computer Science and Engineering Amity School of Engineering and Technology Amity University Uttar Pradesh Lucknow Campus India

ISBN: (纸本)9781450399937

Human Pose Estimation is a method of detecting and classifying all the key human body joints. A 2D Human Pose Estimation method predicts the two-dimensional coordinates from an input image. Classical methods used deformable parts models for detecting body joints. This paper discusses the state-of-the-art Deep Learning based approaches for estimating human poses. It can be implemented by either using the top-down or bottom-up technique for pose estimation. Here, the authors have studied the Convolutional Pose Machines, Hourglass Network and MoveNet architectures for Pose Estimation. These architectures are compared against each other in terms of their efficiency by measuring the time taken by each of them and their accuracy by evaluating them using the Percentage of Detected Joints metric on the MPII dataset. The architectures were implemented by using TensorFlow and Open-Source libraries built using PyTorch. The inference results for each joint were compared individually for all the body joints. The Hourglass Network showed the best accuracy but is really slow. The MoveNet architecture is the second best according to accuracy but is very fast and can be used in real-time systems.

关键词： Deep Learning

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Optimizing the Capacity of Extreme Learning Machines for Biomedical Informatics Applications 1

Optimizing the Capacity of Extreme Learning Machines for Bio...

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1st IEEE International Conference on Emerging Research in Computational science, ICERCS 2023

作者： Logeshwaran, J. Bhardwaj, Rajat Salagrama, Shailaja Das, Abhijit Sri Eshwar College of Engineering Department of Electronics and Communication Engineering Tamil Nadu Coimbatore India University of the Cumberland's Department of Information Technology Kentucky Williamsburg United States Amity University Aset Department of Computer Science and Engineering Karnataka Bengaluru India Rv University School of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350359756

the paper discusses a way of growing the capability of severe deep learning machines (ELM) for biomedical informatics programs. This technique involves varying the dimensions of the training set, the variety of neurons in the hidden layer, the kind of activation function used, the range of different sorts of neurons used and the regularization techniques *** that it will maximize the ability of ELM fashions for biomedical informatics applications, the authors advocate using a stepwise seek technique to determine the fine combination of parameters. This technique entails first defining a baseline or initial configuration of ELM parameters and then iteratively enhancing that configuration via deep learning from the empirical effects of each optimization step. distinctive regularization strategies are carried out at every optimization step which could involve pruning hidden neurons as a way to reduce the complexity of the model. The authors verified their technique on publicly to be had datasets consisting of the Michigan Imputation of missing Values (MIMV) dataset and on a dataset of clinical records from the branch of Anesthesiology, branch of health and Human services. © 2023 IEEE.

关键词： Neurons

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LPG Cylinder Leakage Monitoring by IoT 6

LPG Cylinder Leakage Monitoring by IoT

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6th International Conference on Inventive Computation Technologies, ICICT 2023

作者： Chakradhar, K.S. Deshmukh, Revendra Singh, Pavitar Parkash Hazela, Bramah Taluja, Resham Electronics and Communication Engineering Mohan Babu University Tirupathi517102 India Department of Mechanical Engineering Chhatrapathi Shivaji Institute of Technology Bhilai India Department of Management Lovely Professional University Jalandhar Delhi India Amity School of Engineering & Technology Amity University Lucknow India Department of Computer Science and Engineering Graphic Era Hill University Dehradun India

ISBN: (纸本)9798350398496

This study aims to set up a system that will watch for leaks of liquid petroleum gas in both homes and businesses. This data will update through IoT connectivity to the user's web browser in the case of a gas leak. Using IoT, the user may keep a constant eye on the data. The MQ06 sensor is a gas detector that is sensitive to LPG. LPG gas is detected using an Arduino-based Internet-of-Things (IoT) leak detection device. Interfacing an LPG gas sensor with an Arduino is the main focus of this research study. This is the sensor that sends the signal to the Arduino microcontroller. The microcontroller is in link to an indicator light (LED), an alarm (buzzer), and the Internet of things (ESP8266) through an IOT module. The ESP8266 chip is to make this Internet of Things (IoT) LPG leak detector work. This Wi-Fi module may link microcontrollers to a wireless network, allowing them to access the Internet and transmit and receive data through TCP/IP. Afterwards, the IoT receives emotional data. Using the IoT module, the web may send information. When the detector picks up a gas leak and turns on the device, the site will show a "leak" message. Installations of an IoT and Arduino-powered LPG leak monitoring system are suitable for dwellings, lodging establishments, and LPG cylinder storage locations. The main benefits of this project are that leakage can be found during monitoring, and corrective action data can send to a website for analysis. IoT reports may prevent injuries and property damage if appropriate measures take promptly. © 2023 IEEE.

关键词： Liquefied petroleum gas

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Milling Force Prediction of Titanium Alloy Based on Support Vector Machine and Ant Colony Optimization

IAENG International Journal of Computer Science

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IAENG International Journal of computer science 2021年第2期48卷 1-12页

作者： Zhang, Xiaoxia Yu, Ziqiao Hu, Yinyin Yang, Jiao A Professor of School of Computer Science and Software Engineering University of Science and Technology LiaoNing Anshan11041 China A Master Student of School of Computer Science and Software Engineering University of Science and Technology LiaoNing Anshan114051 China

Milling force prediction of titanium alloy plays an important role in titanium alloy milling process. In the article, the milling process of titanium alloy materials is analyzed, and this material quality is affected directly by the milling force. Support vector machine (SVM) has shown prominent performance for many real-world problems. However, SVM computational complexity increases, as the number of samples increases. How to effectively apply it to massive datasets is still a serious challenge. Although there are some literatures on parameter optimization techniques of SVM regression, the performance of this model still needs to be further studied and improved. We present a new milling force predicting model, namely ACO&SVM, which integrate SVM regression with ACO algorithm to enhance the prediction accuracy of milling force for mill process of titanium alloy. The primary innovation feature of hybrid model is to apply the ACO to automatically determine parameters of SVM regression model. In addition, as being applied to discrete optimization, to solve continuous optimization problems, ACO algorithm needs to transform continuous variables into discrete ones by discretizing of continuous variables. The results have shown that proposed ACO&SVM model yields better prediction accuracy, and thus it can be widely applied to the fields of material processing optimization. © 2021. All Rights Reserved.

关键词： Titanium alloys

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Machine Learning Role in Cognitive Mental Health Analysis amid Covid-19 Crisis: A Critical Study

Machine Learning Role in Cognitive Mental Health Analysis am...

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2022 International Conference on Machine Learning, Big Data, Cloud and Parallel Computing, COM-IT-CON 2022

作者： Aggarwal, Riya Girdhar, Nancy Alpana Amity University Computer Science and Engineering Uttar Pradesh Noida India Bennett University School of Computer Science Engineering and Technology UP Greater Noida India Manav Rachna University Computer Science and Technology Haryana Faridabad India

ISBN: (数字)9781665496025

ISBN: (纸本)9781665496025

Coronavirus is a disease caused by SARS-CoV-2, which can cause severe respiratory problems in humans. World Health Organization declared it to be a pandemic, as per the rate of spread and scale of its transmission. The mental health of people is impacted rudely by Covid-19. The influence of the Covid19 virus on psychological health leads to depression, anxiety, posttraumatic stress, dementia, mental stress, helplessness, fear of losing, etc. Machine Learning is performing a critical role in the rapid advancement of the healthcare system in the past few years. Machine Learning techniques are employed to forecast, diagnose disease, evaluate data by studying the earlier data, and construct different patterns of it. Therefore, the purpose of this report is to address the issue of psychiatric illnesses by identifying those who are at an elevated risk of mental conditions, due to increased stress throughout the Covid19 crisis. Due to the current ongoing pandemic, the mental health crisis needs time, and proactive interference to confront and endure the anxiety. In this research, A comprehensive literature survey was undertaken to examine some machine learning predictive models for primitive prediction of a particular type of mental illness using machine learning algorithms. Several existing research papers were reviewed and after evaluation results show that among various algorithms like, Gradient Boosting Machine, Support Vector Machines, Naïve Bayes, K-Nearest Neighbors;Support Vector Machine (98.6%), Random Forest, and Random Forest (97.07%) are the most accurate algorithm for predicting mental illness amid the ongoing pandemic. © 2022 IEEE.

关键词： Support vector machines

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Automated Training Techniques and Electronics Sensors Role in Cricket: A Review

Automated Training Techniques and Electronics Sensors Role i...

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International Conference on Smart Technologies for Sustainable Development 2021, ICSTSD 2021

作者： Balbudhe, Pravin Khandelwal, Dr. Brijesh Solanki, Dr. Sachin Department of Computer Science & Engineering Amity School of Engineering and Technology Amity University Raipur Chhattisgarh India Directorate of Technical Education Government Polytechnic Campus Maharashtra Sadar Nagpur India

This paper presents the study about technological involvement in game coaching. Attending multiple players with their performance and accuracy level checking is not feasible for coaches every time. Self-paced training sessions or self-learning methods are invented by different researchers & identify multiple games or the gaming apparatuses for different level automation. Methods used for analysis purpose & described the smart cricket ball & its circuit diagram. Tracking technology that are used in cricket, tennis, Gaelic football, badminton, hurling, rugby union, association football & volleyball, to visually track the trajectory of the ball, Centre of Percussion (COP) in cricket, Accelerometer & Swing angle model. Provides a systematic literature review of smart sport & various methods i.e, SVM, CART, ML, AI, CNN, SVM, ORB, SIFT & SURF. Lastly, future directions of research are proposed in the emerging field of SST. © Published under licence by IOP Publishing Ltd.

关键词： Internet of things

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An approach to summarize multilingual news using deep learning technique

An approach to summarize multilingual news using deep learni...

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8th IET Smart Cities Symposium (SCS 2024)

作者： Sumita Gupta Sapna Gambhir Rana Majumdar Department of Computer Science & Engineering Amity School of Engineering & Technology Amity University Noida India Department of Computer Science College of Engineering and Computing George Mason University USA Department of Computer Science Sister Nivedita University India

ISBN: (数字)9781837243105

Every single day the volume of data is growing exponentially high aiding more than half of the overall digital content. As now technologies are getting advanced and computing infrastructure is affordable, the digital content is catastrophically growing. The most critical challenge is to distinguish between the structured and unstructured data available in the form of text, image, audio, or videos etc on the internet where most of the content are unstructured. In this paper, deep learning algorithms are used for training and testing the data. Also, news translation and summarization from Hindi website to English is done for those people who are fond of news on the web and are not interested to go through the entire news instead they are looking for highlights or summary of those news. So, the automatic summarization of news articles is required. Here, an automatic text summarizer using extractive summarization approach is proposed and implemented by considering deep learning for categorization of news content and text rank algorithm for summarization. To evaluate accuracy, F-measure and recall of the produced summary, deep learning algorithms are applied. The result produced 99.4% of accuracy using LSTM over other deep learning like CNN, LSTM, CNN-LSTM, BI-LSTM etc.

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Strategic Decision Making in the AI Age: Examining Neural Network Models in Business Management

Strategic Decision Making in the AI Age: Examining Neural Ne...

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2024 International Conference on Communication, computer sciences and engineering, IC3SE 2024

作者： Jamalpur, Bhavana Sarkar, Prithu Krishna, M. Hari Lourens, Melanie Lakshmi, K. Shantha Singh, Navdeep School Of Computer Science And Artificial Intelligence Sr University Telangana Warangal India Amity School Of Communication Amity University Kolkata India Institute Of Aeronautical Engineering Dundigal Hyderabad India Durban University Of Technology Faculty Of Management Sciences South Africa M. Kumarasamy College Of Engineering Department Of Mathematics Karur India Lovely Professional University Phagwara India

ISBN: (纸本)9798350366846

Over the last 10 years, developments in machine learning techniques and artificial intelligence (AI) tools have garnered interest from corporations and contemporary literature. However, even if AI technology has a lot of promise to resolve problems, there are still issues with its practical use and a dearth of knowledge on how to use it strategically to generate commercial value. Businesses can quickly gather and analyse large quantities of data with the help of AI-powered systems, which facilitates speedier and better-informed decision-making. All things considered, incorporating AI into business decision-making has the power to propel organisational performance and influence how business is conducted in the future. © 2024 IEEE.

关键词： Neural network models

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Performance evaluation of neural network for human classification using blob dataset

Recent Advances in Computer Science and Communications

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Recent Advances in computer science and Communications 2021年第5期14卷 1592-1602页

作者： Mehta, Monika Bhadauria, Madhulika Department of Computer Science & Engineering University School of Information Communication & Technology Guru Gobind Singh Indraprastha University Dwarka New Delhi India Amity School of Engineering & Technology Amity University Noida India

Background: Human Classification in public places is an emerging area in the applications of Computational Intelligence. Therefore, modeling of an optimal architecture of the neural network is required to classify them. Methods: In this work for this purpose, blob dataset has been used to train the neural network. This dataset consists of 2408 features of a human blob. Results: Further, analysis of this blob dataset has been done on the basis of various characteristic pa-rameters for affirmation of actual training. During training and testing of this dataset, it has been observed that when nodes at hidden layer are below and above 10 then training of neural network is under fitted and overfitted respectively and works effectively when the nodes are 10 at the hidden layer. Conclusion: From the experimental work performed in this study, an optimal neural network has been obtained to classify human using blob dataset. © 2021 Bentham science Publishers.

关键词： Pattern recognition

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