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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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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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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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Optimizing Reliability of Smart Healthcare Monitoring Systems via Network Coding in Wireless Body Area Networks

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Internet technology Letters 2025年第4期8卷

作者： Dev, Maha Singh, Saurabh Jain, Abhishek Kumar, Ashok Chaudhary, Jitendra Kumar Chandigarh University Mohali India School of Computer Science and Engineering Galgotias University Greater Noida India Department of Computer Science and Engineering Uttaranchal University Dehradun India Amity School of Engineering and Technology Amity University of Madhya Pradesh Gwalior India School of Computing Graphic Era Hill University Bhimtal India

A Wireless Body Area Network (WBAN) is a system that connects a variety of self-contained medical sensors and devices positioned both internally and externally on the human body. The reliability of the network is an important consideration in the design of a WBAN, especially for healthcare applications. Because of the network's close proximity to the human body, electromagnetic waves should have a low effect on the body. In a WBAN, sensors do not send their data directly to monitoring devices or gateways;instead, the data is transmitted via relay nodes. A new smart healthcare monitoring reliability system based on cooperative network coding (RCNC) is presented. Simulation results demonstrate that RCNC-WBAN schemes can achieve optimal results while improving packet delivery, reducing resource redundancy degree (RRD), and minimizing delay. © 2025 John Wiley & Sons Ltd.

关键词： Wireless local area networks (WLAN)

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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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Customer Retention Using Machine Learning 10

Customer Retention Using Machine Learning

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10th IEEE Uttar Pradesh Section International Conference on Electrical, Electronics and computer engineering, UPCON 2023

作者： Das, Lipsa Salman, Rahama Sabeer, Shaista Ansari, Subuhi Kashif Aarif, Mohd Rana, Ajay Amity School of Engineering and Technology Amity University UP Greater Noida India Jazan University Department of Computer Science Saudi Arabia Global Research Network UP Noida India College of Computer Science & Information Technology Jazan University Department of Information Technology and Security Jazan Saudi Arabia College of Computer Science and Information Technology & Security Jazan University Jazan Saudi Arabia

ISBN: (纸本)9798350382471

Maintaining customer retention stands as a pivotal element in ensuring business success, given that the costs associated with acquiring new customers often surpass those linked to retaining existing ones. Recognizing the significance of retaining a loyal customer base, businesses prioritize strategies and initiatives that cultivate lasting relationships with their current clientele, understanding that sustained customer loyalty contributes substantially to long-term viability and profitability.. Machine learning techniques offer a promising approach to predicting customer behavior and developing personalized retention strategies. In this research paper, we explore the use of machine learning for customer retention, specifically focusing on predictive modeling and recommendation systems. We review recent literature on the topic, discussing different machine learning algorithms and techniques that have been applied to customer retention. We also present a case study of a company that has successfully implemented machine learning-based retention strategies, highlighting the benefits and challenges of this approach. Our findings suggest that machine learning can be an effective tool for improving customer retention, but its success depends on several factors, including data quality, model accuracy, and implementation strategy. Our conclusion involves an exploration of the consequences of our research for forthcoming studies and practical applications within business environments.. © 2023 IEEE.

关键词： customer behavior Customer retention machine learning predictive modelling

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Path Detection for Self-Driving Carts by using Canny Edge Detection Algorithm 9

Path Detection for Self-Driving Carts by using Canny Edge De...

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9th IEEE International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions), ICRITO 2021

作者： Srivastava, Sumir Gupta, Sumita Amity School of Engineering and Technology Amity University Department of Computer Science and Engineering Noida India

ISBN: (纸本)9781665417037

A lane detection system is the most principal component of a self-driving transportation computation system. It deals with various complex scenarios such as lane curvature worn out marking, lane changing, turning U-turns, ending and separating lines. Many modern corporations and enterprises like Tesla have been able to achieve second grade i.e., partial automation at a commercial level. In this research paper, an improved and optimized lane detection method is proposed by using the OpenCV's canny edge detection algorithm. A quick and robust method that can easily detect lanes in a live video feed or a pre-recorded video stream. This proposed framework effectively combines the OpenCV's canny edge algorithm with the Hough transform function so it can be applied in small scale applications specifically for visitor carts in an apartment complex. Testing and analysis of the program shows that the suggested software is very reliable. © 2021 IEEE.

关键词： Hough transforms

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