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A Comprehensive Review of the Application of Greenhouse Using the Internet of Things 1

5th International Conference on computer and Communication Technologies, IC3T 2023

作者： Singh, Kamred Udham Kumar, Ankit Kumar, Gaurav Singh, Teekam Choudhury, Tanupriya Kumar, Santosh School of Computing Graphic Era Hill University Dehradun India Department of Computer Engineering and Applications GLA University Mathura India Department of Computer Science and Engineering Graphic Era Deemed to Be University Dehradun India Lavale Campus Pune India Department of Management Jaipuria Institute of Management Jaipur India CSE Dept. Graphic Era Deemed to be University Uttarakhand Dehradun248002 India

ISBN: (数字)9789819997077

ISBN: (纸本)9789819997060

The primary objective of researching the Internet of Things as a tool is to regulate and oversee agriculture in order to facilitate the growth of a greater variety of crops and an increase in the quantity of crops produced overall. Farmers are subjected to a wide range of challenges, including uneven distribution of water, unstable weather conditions (such as wind, hailstorm, UV radiations, and pesticide assaults), and a diverse range of soil types. Because water should be spread in an equal manner, it becomes extremely difficult to control the uneven distribution of water;hence, a new irrigation system has to be created in order to circumvent this challenge. Greenhouse has shown to be a very helpful tool in resolving all of these issues. An inclined technology is one that utilizes the Internet of Things in order to make greenhouse a more adaptable platform. In this case, a GSM module is used for the purpose of managing the tube well, in addition to a fogger and a variety of sensors for regulating the temperature and the relative humidity. Large regions are now covered by sensor networks, which is helpful since it allows for the creation of greenhouses with realistic environments, which plays a significant role in the development of crops. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Sensor networks

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An Expert System for Clinical Risk Assessment of Polycystic Ovary Syndrome under Uncertainty

Research Square

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Research Square 2023年

作者： Karim, Rezaul Khaliluzzaman, Md. Mahmud, Tanzim Chy, Deepak Kumar Dept. of Computer Science and Engineering University of Chittagong Chittagong4331 Bangladesh Dept. of Computer Science and Engineering International Islamic University Chittagong Chittagong4203 Bangladesh Dept. of Computer Science and Engineering Textile Engineering College Begumgonj Noakhali3821 Bangladesh Dept. of Electrical & Electronics Engineering University of Information Technology & Sciences Dhaka1212 Bangladesh

This paper describes a prototype clinical expert system for risk stratification of patients with polycystic ovary syndrome (PCOS). Polycystic ovary syndrome (PCOS) is the most common hormonal disorder among women of reproductive age. It is a heterogeneous disorder of uncertain cause. Since the symptoms of PCOS are seemingly unrelated to one another the condition is often overlooked and undiagnosed. The determination of accurate degree or intensity of PCOS signs is difficult for the physician. Hence, the accuracy of the diagnostic process is difficult to achieve. The signs and symptoms of PCOS are usually expressed in qualitative and quantitative ways. Since the qualitative factors cannot measure in a quantitative way, various types of uncertainties may occur, such as incompleteness, vagueness and imprecision. For that, it is necessary to address the issue of uncertainty by using appropriate methodology. However, no existing system is able to address this issue of uncertainty. Therefore, this paper demonstrates the application of a novel method, named belief rule-based inference methodology-RIMER;this prototype can deal with uncertainties in both clinical domain knowledge and clinical data. This paper reports the development of a Belief Rule Based Expert System (BRBES) using RIMER approach, which is capable of detecting the PCOS by taking account of signs and symptoms. © 2023, CC BY.

关键词： Expert systems

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Performance Analysis of Different Hardware Platforms in Wireless Sensor Networks

Performance Analysis of Different Hardware Platforms in Wire...

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International Conference on Signal Processing and Communication (ICSPC)

作者： P. Esther Jebarani Amir Anton Jone Tony Jose J. John Paul Shelja Jose Pradeep Kumar Dept. of Computer Science Kovai Kalaimagal College of Arts and Science Coimbatore India Dept. of Electronics and Communication Engineering Karunya Institute of Technology and Sciences Coimbatore India Dept. of Electronics and Communication Engineering Indira Gandhi Institute of Engineering and Technology Kothamangalam India

Wireless Sensor Networks (WSN) encompasses rapid development in research area through large scale applications. The main objective is to compare four different hardware platforms such as Rene, Mica, Spec and Blue in order to find efficient hardware platform that can provide best performance within certain constraints while working on TinyOS as operating system. The four different hardware platforms are compared on the basis of certain constraints such as data communication, rendezvous(mJ/day), alarm checking, sensing, sleep (units in mJ/day), expected lifetime (units in years). According to this investigation, mica outperforms the four different platforms in terms of decoupling between RF and data path speed, bit rate, and timing accuracy. However, future specifications will be found to be more efficient based on CMOS processes and RF radio technology, which results in lower costs, smaller sizes, lower power requirements, and a decrease in complexity.

关键词： Radio frequency Wireless communication Concurrent computing Wireless sensor networks Power demand Costs Operating systems

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Artificial Intelligence driven Deep Learning for Competitive Intelligence to enhance Market Analysis and Strategic Positioning 4

Artificial Intelligence driven Deep Learning for Competitive...

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4th IEEE Asian Conference on Innovation in technology, ASIANCON 2024

作者： Shukla, Sunil Singh, Jagendra Nassa, Vinay Kumar Saba, Masarath Bhatia, Jitesh Elangovan, Muniyandy Graphic Era Hill University School of Computing Dehradun India Bennett University School of Computer Science Engineering & Technology Greater Noida India Ggsi University Tecnia Institute of Advanced Studies Dept. of Information Communication Technology Delhi India Hyderabad India Epsilon Department of Client Engagement MA United States Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Biosciences Chennai India

ISBN: (纸本)9798350354218

This research provides insight into the successful application of modern deep learning techniques such as Transfer Learning, BERT, and Autoencoders to develop a powerful tool to sustain market analysis and strategic positioning in competitive intelligence. The conducted analysis proved that BERT was the most effective technique, as it produced the highest results in regards of accuracy, precision, recall, and F1 score values in all ten trials. The results of the experiment showed that with accuracy ranging from 90.5% to 92.3%, precision from 91.2% to 93.0%, recall from 89.7% to 91.7%, and F1 score from 90.4% to 92.2%, BERT proved to be the best technique for extracting insights from various textual data sources. In and of itself, Transfer Learning also yielded promising results, and its accuracy ranged from 85.2% to 88.6%, precision from 86.5% to 89.9%, recall from 83.7% to 87.3%, and F1 score from 85.0% to 88.5%. Additionally, given its ability to adapt to particular tasks with quite limited labeled data, Transfer Learning is a valuable tool for market analysis. In this experiment for Autoencoders, it has been shown that they provided a slightly lower performance than BERT and Transfer Learning. The reconstruction error of the deep learning method used in this experiment ranged from 0.018 to 0.025, while accuracy varied from 87.9% to 90.3%. Nevertheless, further optimization can make autoencoders a promising approach for market analysis. Thus, this research indicates the vital importance of leveraging advanced deep learning techniques for gaining a competitive advantage in the present-day business environment. © 2024 IEEE.

关键词： Contrastive Learning

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Fairness in Healthcare: Assessing Data Bias and Algorithmic Fairness

Fairness in Healthcare: Assessing Data Bias and Algorithmic ...

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Medical Information Processing and Analysis (SIPAIM), International Symposium on

作者： Farzaneh Dehghani Nikita Malik Joanna Lin Sayeh Bayat Mariana Bento dept. Biomedical Engineering University of Calgary Calgary Canada dept. Information Technology Manipal Institute Of Technology MAHE Manipal India dept. Computer Science University of Calgary Calgary Canada dept. Biomedical Engineering dept. Geomatics Engineering University of Calgary Calgary Canada dept. Biomedical Engineering dept. Electrical and Software Engineering University of Calgary Calgary Canada

ISBN: (数字)9798331528652

ISBN: (纸本)9798331528669

With the wide employment of Artificial Intelligence-based systems in healthcare, there is an increasing need to further quantify and analyze potential biases in data and algorithms, mitigating health disparities. We aim to investigate publicly available healthcare datasets to identify potential biases, and investigate the impact of these biases on the predictive performance of the models. The results indicate that bias in these datasets exerts a negative impact on the fairness of the learned models. In the context of a highly imbalanced dataset, we further investigate the impact of various imbalance handling techniques on both the performance and fairness of the models. This study underscores the importance of early detection of bias to mitigate the risk of introducing such biases in real-world applications, particularly within a sensitive domain, such as healthcare.

关键词： Measurement Prevention and mitigation Decision making Pipelines Medical services Predictive models Stroke (medical condition) Prediction algorithms Reliability Artificial intelligence

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Enhanced Lane Detection for Autonomous Campus Shuttles Using Hybrid computer Vision Techniques

Enhanced Lane Detection for Autonomous Campus Shuttles Using...

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2024 International Conference on science, engineering and Business for Driving Sustainable Development Goals, SEB4SDG 2024

作者： Aderoba, Olugbenga Adegbemisola Ekezie, Gideon Onyinyechi Mpofu, Khumbulani Ajisegiri, Emmanuel Adediran, Adeolu A. Daniyan, Ilesanmi Afolabi Ogundokun, Roseline Oluwaseun Landmark University Department of Mechanical Engineering Omu-Aran Nigeria Tshwane University of Tech Dept. of Industrial Engineering Pretoria South Africa Tshwane University of Technology Department of Industrial Engineering Pretoria South Africa University of Johannesburg Department of Mechanical Engineering Science South Africa Achievers University Department of Mechanical and Mechatronics Engineering Owo Nigeria Landmark University Department of Computer Science Omu-Aran Nigeria Kaunas University of Technology Department of Multimedia Engineering Lithuania

ISBN: (纸本)9798350358155

Ensuring safe and efficient navigation in smart campus transportation systems is increasingly crucial with the integration of autonomous vehicles, making accurate and robust lane detection a paramount factor. This research presents a combined traditional computer vision algorithm with advanced deep learning techniques to enhance the precision and dependability of lane detection in diverse campus environments in autonomous campus shuttles. The system utilizes a multi-stage process, incorporating color segmentation, edge detection, and Convolutional Neural Network (CNN) for robust lane feature extraction. A dynamic region of interest adjustment mechanism that adapts to varying campus scenarios ensuring adaptability to lighting conditions and road layouts, was introduced. Experimental results using a miniature scaled-down autonomous campus shuttle demonstrated significant improvements in lane detection accuracy compared to conventional methods, validating the effectiveness of our enhanced approach. The proposed system enhances the safety and reliability of autonomous campus shuttles and contributes to the broader field of computer vision applications for intelligent transportation systems. © 2024 IEEE.

关键词： Convolutional neural networks

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CivilityCheck: An Integrated Natural Language Processing and Machine Learning Framework to Detect Hateful and Offensive Language

CivilityCheck: An Integrated Natural Language Processing and...

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Sustainable Computing and Smart Systems (ICSCSS), International Conference on

作者： Bhulakshmi Bonthu Potluri Abhay Likitha Sai Gottipati Gangisetty Krishna Vamsi VIT Vellore School of Computer Science and Engineering Vellore Institute of Technology Vellore India Dept. of AI &DS Seshadri Rao Gudlavalleru Engineering College Gudlavalleru India

ISBN: (数字)9798350379990

ISBN: (纸本)9798350391558

The proliferation of hate speech and offensive language in online communities has escalated into a significant societal concern, prompting an urgent demand for robust tools capable of automatically identifying and addressing such content. Introducing CivilityCheck, a comprehensive framework that seamlessly integrates Natural Language Processing (NLP) and Machine Learning (ML) methodologies to uncover instances of hate speech and offensiveness within online discourse. Utilizing logistic regression models trained on Twitter datasets, CivilityCheck demonstrates remarkable efficacy, achieving a notable test accuracy of 95.4%. Moreover, the proposed framework extends its functionality into a user-friendly web browser extension, facilitating real-time analysis of social media content to cultivate a safer online environment. By endowing users with the capability to promptly identify and address toxic language, CivilityCheck contributes significantly to the propagation of civility and respect in digital interactions. This innovative solution stands at the forefront of combating online toxicity, offering a potent means to foster constructive dialogue and mitigate the harmful effects of coarse and regressive language usage on digital platforms.

关键词： Logistic regression Accuracy Toxicology Social networking (online) Hate speech Refining Machine learning

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Characterization and Control of a Superconducting Homopolar Motor with Cryogenic GaN Inverter for Electric Aircrafts

Characterization and Control of a Superconducting Homopolar ...

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2024 IEEE Energy Conversion Congress and Exposition, ECCE 2024

作者： Gliese, Felix Pearce, Matthew G. S. Wadsworth, Aaron Siamaki, Mohammad Storey, James Lumsden, Grant Thrimawithana, Duleepa J. Badcock, Rod Roth-Stielow, Joerg Neuburger, Martin Esslingen University Faculty of Mobility and Technology Germany The University of Auckland Dept. of Electrical Computer and Software Engineering New Zealand OpenStar Technologies New Zealand Victoria University of Wellington Robinson Research Institute New Zealand University of Stuttgart Institute for Power Electronics and Electrical Drives Germany

ISBN: (纸本)9798350376067

In the quest for a zero-emissions future, the aviation sector is facing the challenge of reducing its carbon footprint. This paper presents a characterization of a superconducting homopolar motor designed to meet the stringent power and efficiency requirements of all-electric aircraft. The control system, which features field-oriented control and a multi-layer architecture, ensures precise operation of the motor under various load conditions. The motor uses a stationary cryogenic superconducting DC coil to generate the rotor's magnetic field. Utilizing superconducting materials helps produce the high power densities critical for aviation. The characterization of the key parameters of the motor, such as inductances, stator resistance and flux, are presented. A description of the cryogenic GaN power electronic inverter power stage immersed in liquid nitrogen is provided. This design enables future integration next to the motor, and improved power density and efficiency. This paper outlines the design, implementation, and initial testing of the superconducting homopolar motor and its power electronics, highlighting its potential for electric propulsion in aviation by providing a viable solution for long-range flights. © 2024 IEEE.

关键词： Gallium nitride

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Metamaterial Assisted Multi-Wide Band Electrically Small Antennas

Metamaterial Assisted Multi-Wide Band Electrically Small Ant...

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International Conference on computer and Information technology (ICCIT)

作者： Sakib Sadman Abdul Khaleque A B M Arafat Hossain Kumary Sumi Rani Shaha Mst. Sumaya Akter Dept. of Electrical & Electronic Engineering Rajshahi University of Engineering & Technology Rajshahi Bangladesh Dept. of Electrical & Computer Engineering University of Houston Houston Texas USA Dept. of Electrical Electronic and Communication Engineering Pabna University of Science and Technology Bangladesh

ISBN: (数字)9798331519094

ISBN: (纸本)9798331519100

Metamaterials have gained significant interest in antenna technology nowadays due to their exceptional features. This article presents metamaterial (MTM) based two new antennas with a vision to precisely enhance the number of bands and maintain a suitable bandwidth level. A sequential rise in band numbers from 2 to 4 and then to 5 is noticed by the applications of split-ring resonator (SRR) and complementary structure of dual negative metamaterial (dual NG-MTM) in a standard patch antenna. Initially, a dual NG-MTM was attached at the back of the patch antenna and it presented four separate bands at 2 GHz, 6.8 GHz, 9.1 GHz, and 9.6 GHz, respectively with comparatively larger bandwidth. At last, inclusion of an SRR takes the band-width to another level in addition to providing five bands and its electrical size is then compact as 0.22λ 0 × 0.19λ 0 × 0.0006λ 0 , with a ka (antenna size indicator) value of less than 1. These extraordinary features of the above-mentioned MTM antennas lead to promising applications including television broadcasting and 5G system.

关键词： Wireless LAN TV 5G mobile communication Patch antennas Bandwidth Broadcasting Metamaterials Resonance Information technology Standards

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Multi mode Selection Sending Messages to Prioritize Nodes using WiFi Direct in Post Disaster Situation

Multi mode Selection Sending Messages to Prioritize Nodes us...

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Sustainable Energy, Signal Processing and Cyber Security (iSSSC), IEEE International Symposium on

作者： Balaram Pal Ashok Kumar Turuk Bibhudatta Sahoo Sourabh Debnath Jitendra Kumar Dept. of Computer Science Engineering National Institute of Technology Rourkela Odisha

ISBN: (纸本)9781665490573

In recent times WiFi Direct is a technology that can enable direct-to-direct device communication, single-group communication, or multi-group communication with each other device without any access point or without any internet access point. This paper expresses the WiFi Direct based multi-mode selection mobile ad hoc network. Basically here is a gateway layer and routing layer developed for supporting single group communication or multi-group communication between each WiFi direct supported device. This paper describes how to optimize the redundancy, how to select random group owners through WiFi Direct, and how to give priority to messages and among nodes also. Priority nodes have been selected from random nodes.

关键词： Databases Redundancy Software performance Signal processing Logic gates Routing Hardware

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