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Global Transitions Proceedings 2021年第2期2卷 402-407页

作者： Pallavi Kamath Pallavi Patil Shrilatha S Sushma Sowmya S Dept. Computer Science and Engineering Shri Madhwa Vadiraja Institute of Technology and Management (Affiliated to VTU) Udupi 574115 India

India is a heavily reliant on agriculture. Organic, economic, and seasonal factors all influence agricultural yield. Estimating agricultural production is a difficult task for our country, particularly given the current population situation. Crop production assumptions made far in advance can help farmers make the necessary planning for things like storing and marketing. Crop production prediction involves a huge amount of data, making it a perfect candidate for data mining methods. Data mining is method of accumulating previously unseen anticipated information from vast database. Data mining assists in the analysis of future patterns and character, enabling companies to make informed decisions. For a specific region, this research provides a fast inspection of agricultural yield forecast using the Random Forest approach.

关键词： Accuracy Agriculture Crop yield prediction Data mining Random forest algorithm

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A Framework for simulation control Systems Based on Software Product Line

A Framework for simulation control Systems Based on Software...

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IEEE International Symposium on Information (IT) in Medicine and Education, ITME

作者： Xu Huijuan Li Xu Sun Xiaoning Dept. Computer and Artificial Intelligence Huanghuai University ZhuMaDian China Henan International Joint Laboratory of Behavior Optimization Control for Smart Robots ZhuMaDian China Dept. Intelligent Manufacturing Huanghuai University ZhuMaDian China Dept. Economic Management ZhuMaDian Vocational and Technical College ZhuMaDian China

At present, the fast-changing functional requirements of simulation control system in Aerospace and navigation, where various hardware boards are used, have caused the high complexity and long development cycle of control systems. Although such systems have a certain degree of similarity, they are not well reused. In order to solve these problems, this paper puts forward the idea of using the software product line to model the control system of spacecraft and designs a framework. Using this framework, a simulation wave data acquisition software is developed. The application of this framework can achieve maximum granularity of software reuse, and improve the development efficiency.

关键词： Space vehicles Navigation Education Data acquisition control systems Data models Hardware

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Design and simulation of intelligent orchard system based on Internet of Things

Design and simulation of intelligent orchard system based on...

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IEEE International Symposium on Information (IT) in Medicine and Education, ITME

作者： Li Xu Sun Xiaoning Luo Zhipcng Xu Huijuan Dept. Intelligent Manufacturing Huanghuai University ZhuMaDian China Dept. Economic Management ZhuMaDian Vocational and Technical College ZhuMaDian China Dept. Computer and Artificial Intelligence Huanghuai University ZhuMaDian China Henan International Joint Laboratory of Behavior Optimization Control for Smart Robots ZhuMaDian China

Based on the IVS simulation platform, the system uses IOT technology to monitor and control the environmental data onto greenhouse orchards for the purpose of orchard wisdom management. The system realizes the bi-directional function of manual remote as well as intelligent self-control through integrated sensors and wireless communication network, and optimizes through fuzzy control algorithm, PID algorithm and supervised learning algorithm to achieve the monitoring of data such as air temperature and humidity, soil temperature and humidity and carbon dioxide concentration in the park, so that the environment is always maintained in the range suitable for fruit tree growth. Finally, a QR code is added to each batch of fruits to build a traceability function of food safety.

关键词： Temperature sensors Temperature measurement Wireless sensor networks Temperature distribution Green products Humidity Temperature control

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Deep Learning Analytics and Operations Research: Models, Applications and Managerial Implications

Deep Learning Analytics and Operations Research: Models, App...

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International Conference on Contemporary Computing and Informatics (IC3I)

作者： K. Vijayalakshmi Kushagra Kulshreshtha Mahesh Bhong Durgeshwar Pratap Singh C Praveen Kumar Vijay Bhutani Dean of Research Loyola Academy Alwal Secunderabad Telangana Institute of Business Management GLA University Mathura Uttar Pradesh India Dept. of Mechanical Engineering Indira College of Engineering and Management Pune Maharashtra India Dept of Mechanical Engineering Graphic Era Deemed to be University Dehradun Uttarakhand India Department of Computer Science and Engineering Institute of Aeronautical Engineering Hyderabad Telangana India Department of Mechanical Engineering Amity University Greater Noida Uttar Pradesh India

Business analytics refers to the processes, strategies, and procedures that are utilised to derive value for individuals, organisations, and organisations from data. In today's fast-paced and more globalised digital economy, it is essential for decision-making to be driven more by data and to be supported by artificial intelligence (AI) and machine learning (ML). Deep learning (DL) does offer a number of benefits, but it also has a number of limitations that have, up to this point, prohibited businesses from making widespread use of it. Deep neural networks offer the potential of surpassing models from standard machine learning in terms of prediction accuracy, which is one of the reasons why the introduction of deep learning has led this area to undergo a major transformation. This is one of the reasons why the introduction of deep learning has caused this field to undergo a considerable shift. However, according to the findings of our review of the body of recently published research, the number of papers relevant to our industry that make use of operations research is quite low. Consequently, the objectives of this study provide an overview. In a few different case studies, we look at the value that operations research brings to the table by using real data from real-world business activities. Every one of these instances demonstrates improvements in operations research performance above what is possible with traditional machine learning, which ultimately results in gains in direct value. In this paper, we present academics, managers, and practitioners in the field of operations research who are interested in developing their abilities in deep learning and business analytics with suggestions and implications. Our computational investigations highlight the need of bespoke architectures by recommending a one-of-a-kind deep-embedded network as a solution to the problem that standard, out-of-the-box solutions are often not satisfactory.

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Development of the Future Wireless Network Using Cognitive Radio Networks

Development of the Future Wireless Network Using Cognitive R...

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International Conference on Power, Energy, control and Transmission Systems (ICPECTS)

作者： Dattatraya Arun Jadhav M. Amina Begum TDNSS. Sarveswara Rao P. Swetha Pralhad K. Mudalkar K. G. S. Venkatesan Department of Computer Engineering Keystone School of Engineering Pune India Department of ECE Oxford College of Engineering India Department of ECE Sri Vasavi Engineering College AP India Department of CSE St. Martin’s Engineering College Telangana India Bharati Vidyapeeth Institute of Management Kolhapur India Dept. of C. S. E Megha Institute of Engineering & Technology for Women Telangana India

ISBN: (数字)9798331508845

ISBN: (纸本)9798331508852

CRN (cognitive radio network) system works based on the wireless network system. The various kinds of properties of CRN has been highlighted in the study. The process of the implication of the study and the crash of the WSN (wireless network system) is also discussed in the study. The pros and the cons of using the wireless network to use the cognitive radio are also highlighted. Moreover, the problem and the problems of the use of wireless networks in the CRN (cognitive radio network) are also highlighted in the given study.

关键词： Wireless sensor networks Wireless networks control systems computer crashes Cognitive radio Network systems

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Review on Architectures of Convolutional Neural Network 3

Review on Architectures of Convolutional Neural Network

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3rd International Conference on Disruptive Technologies, ICDT 2025

作者： Chawla, Sachin Gupta, Deepa Pippal, Sanjeev Kumar Amity University Amity School of Engineering and Technology UP Greater Noida India Amity University Amity Institute of Information Technology UP Noida India G.L Bajaj Institute of Technology and Management Dept. Computer Science-Artificial Intelligence UP Greater Noida India

ISBN: (纸本)9798331519582

This review provides a broad overview of CNN architectures, focusing on the crucial building blocks and layered structures that form complete networks. It traces the evolution of CNNs, summarizing key publications and highlighting the diverse array of architectures that have emerged in recent years. While simplifying some technical details, the review maintains conceptual rigor to ensure a thorough understanding of CNN principles and development. This review serves as a valuable resource for those seeking to navigate the complexities of CNNs, offering insights into their operational principles, architectural innovations, and potential applications beyond the realm of computer Vision. © 2025 IEEE.

关键词： Convolutional neural networks

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Enhancing False Positive Alert Detection in Security Information and Event management System Using Recurrent Neural Network

Enhancing False Positive Alert Detection in Security Informa...

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International Conference on computer and Information Technology (ICCIT)

作者： Mst. Nishita Aktar Md. Nazmus Sakib Afzal Hossain Ahsan Ullah Kazi Hassan Robin Kh. Mustafizur Rahman Md Tuhin Reza Ali Ameen Md Refat Hossain Debashis Kundu Computer Science & Engineering World University of Bangladesh Dhaka Bangladesh Information Sciences and Manegement Lincoln University Petaling Jaya Malaysia College of Business Irvine Westcliff University California USA Dept. Management Information Systems American International University Bangladesh Dhaka Bangladesh

ISBN: (数字)9798331519094

ISBN: (纸本)9798331519100

An empirical analysis is conducted to reduce the false positive alerts in Security Information and Event management (SIEM) systems. Through the use of recurrent Neural Networks (RNN), research aims to increase alert detection accuracy and reduce false positive rates. In order to reduce false positives in SIEM systems, this study applies a novel combination of well-established techniques, such as Random Forest classifiers, Echo State Networks (ESN), and Term Frequency-Inverse Document Frequency (TF-IDF) for feature extraction. Although a lot of research has been done on these approaches in other fields, they haven’t been tested in the context of SIEM systems using the particular dataset that this study employed, which came from Kaggle. Investigation demonstrates that the ESN-Random Forest combo yields superior outcomes, achieving high precision, a 99.5% detection accuracy, and a considerable decrease in false positive *** limits in introducing the primary dataset, which is forced to depend only on publicly available *** may not adequately portray the nuances and complexity of real Security Information and Event management (SIEM) systems.

关键词： Accuracy Echo state networks Forestry Feature extraction Complexity theory Information technology computer security Random forests

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Feedforward control in the Presence of Input Nonlinearities: A Learning-based Approach

arXiv

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arXiv 2022年

作者： van Hulst, Jilles Poot, Maurice Kostić, Dragan Yan, Kai Wa Portegies, Jim Oomen, Tom Control Systems Technology Section Dept. of Mechanical Engineering Eindhoven University of Technology Netherlands Center of Competency ASM Pacific Technology Netherlands CASA Dept. of Mathematics and Computer Science Eindhoven University of Technology Netherlands Delft Center for Systems and Control Delft University of Technology Netherlands

Advanced feedforward control methods enable mechatronic systems to perform varying motion tasks with extreme accuracy and throughput. The aim of this paper is to develop a data-driven feedforward controller that addresses input nonlinearities, which are common in typical applications such as semiconductor back-end equipment. The developed method consists of parametric inverse-model feedforward that is optimized for tracking error reduction by exploiting ideas from iterative learning control. Results on a simulated set-up indicate improved performance over existing identification methods for systems with nonlinearities at the input. © 2022, CC BY-NC-ND.

关键词： Feedforward control

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Optimizing V2V Communication in 5G/6G Networks with Enhanced Deep Cooperative Q-Learning

Optimizing V2V Communication in 5G/6G Networks with Enhanced...

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Next Generation Communication & Information Processing (INCIP), International Conference on

作者： Priyameet Kaur Keer N. Lakshmi Deepthi Ginni Nijhawan Neeraj Chahuan Ali Albawi Madhu Palati Master of Business Administration New Horizon College of Engineering Bangalore India Computer Science and Engineering (Data Science) Institute of Aeronautical Engineering Hyderabad India Lovely Professional University Phagwara India Lloyd Institute of Engineering & Technology Greater Noida Uttar Pradesh Radiology Techniques Department College of Medical Technology The Islamic University Najaf Iraq Dept. of EEE BMS Institute of Technology and Management Bangalore

ISBN: (数字)9798331528140

ISBN: (纸本)9798331528157

Understanding the physical channel characteristics of V2V communications is important, but it is neither cost-effective nor practical to have full information about them. Since there is always delay and data-loss during communications, which are further accelerated when the wireless channel status and device parameters remain uncalibrated frequently, full or even partial information of the environment is not feasible, even for a short period. Moreover, the vehicular mobility can also introduce abrupt changes to communication. These uncertainties dictate that a system should be well-and adaptively-designed to handle changing information. V2V communications that exploit this information naturally serve as candidates for artificial Intelligence (AI) algorithms. Specifically, there are applications of cooperative learning strategies that are suitable, including machine learning, deep reinforcement learning, and deep multi-agent reinforcement learning. This paper applies novel Enhanced Deep Cooperative Q-Learning (DCO-DQN) model for V2V communication, to achieve the best trade-offs (latency/ reliability/safety) under various states, including short and long memory, asymptotic and non-asymptotic, possible and unpredictable outcomes scenarios. The proposed framework incorporates spatial wisdom and historical information into Euclidean spaces, accounting for the above behavior and challenges. For this reason, future V2V communication in 5G/6G networks may benefit from adopting our approach, as previously described. A thorough comparative study has been conducted to illustrate our advantages and shortcomings. Our analysis also offers insights on hybrid techniques and decentralized approaches, as possible means for future development.

关键词： Wireless communication 6G mobile communication Q-learning Uncertainty 5G mobile communication Vehicular ad hoc networks Interference Linear programming Safety Optimization

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Smart Complaint Redressal System Using Ethereum Blockchain

Smart Complaint Redressal System Using Ethereum Blockchain

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2020 IEEE International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics, DISCOVER 2020

作者： Jattan, Suhani Kumar, Vineeth Akhilesh, R. Naik, Rachith R. Sneha, N.S. Shri Madhwa Vadiraja Institute of Technology and Management Dept. Computer Science and Engineering Bantakal Udupi India

ISBN: (纸本)9781728198859

In today's world, more importance is given on the availability of the applications and various websites available in the digital market. People will manage their daily work on time, precisely, very fast, and with satisfaction. So various technologies are used to fulfil daily work. In India, there is no direct and efficient way of communication between the government and the public, for solving a problem i.e for getting a problem solved at any place, people may have to wait for three months, but it can probably be solved sooner. Nowadays, the scenario has changed. Many applications are available, which allow users to register their complaints. But there are some problems related to its transparency. This paper proposes an Ethereum blockchain application that will help people to register their complaints and get updates about the complaint. Adoption of blockchain technology makes the application more secure, transparent and immutable. © 2020 IEEE.

关键词： Blockchain

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