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Recommendations for a Vertical Farming System Using Hydroponics, machine learning and IoT 4

Recommendations for a Vertical Farming System Using Hydropon...

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4th international conference on Data Analytics for Business and Industry, ICDABI 2023

作者： Mosaad, Basmala Abdulla, Raed Rana, Muhammad Ehsan School of Engineering Kuala Lumpur Malaysia School of Computer Computing Kuala Lumpur Malaysia

ISBN: (纸本)9798350369786

Agriculture nowadays faces several problems because of the rise of the global population, which increased the food demand and caused instability in food security. Organic food that is pesticide-free is also in high demand, leading to consuming more arable lands, water, pesticides, and fossil fuels. Vertical farming is a promising solution to these challenges. It is a method of growing plants in vertically stacked layers, which can be done indoors or outdoors. Hydroponics is a method of growing plants without soil, using a nutrient-rich water solution. Vertical farming using hydroponics can solve these problems by integrating IoT and AI technology. It reduces water wastage and eliminates pesticide consumption. the aim of this research is to develop recommendations for a Vertical Farming System using hydroponic based on machine learning and IoT technology for a better, controlled, and monitored growth of plants. the hydroponic system and its parameters are monitored and controlled by a GUI using the Blynk app that allows the user to control the food quality by its nutrients and parameters to help in producing more organic food. Crop yield health is being predicted by an AI model that detects the healthy and unhealthy leaves with an accuracy of 82%, hence saving many farmers' time. the system is also applicable for personal use, allowing individuals or families to plant their own food. © 2023 IEEE.

关键词： Hydroponics

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Towards Detecting the Fake Profile in Social Media using Hybrid Model of LSTM and Extreme learning machine 4

Towards Detecting the Fake Profile in Social Media using Hyb...

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4th international conference on Electronics and Sustainable Communication Systems, ICESC 2023

作者： Modak, Santanu Mulla, Zameer Ahmed S. Verma, Aditya Sunalini, K.K. Ali Khan, P.M.D. Gupta, Prashant Swami Vivekananda University School of Computer Science West Bengal India R. M. Dhariwal Sinhgad Management School Kondhapuri-Pune Maharashtra India Graphic Era Hill University Department of Computer Science and Engineering Uttarakhand Dehradun India Koneru Lakshmaiah Education Foundation Andhra Pradesh Guntur India Mohan Babu University School of Computing Andhra Pradesh Tirupati India Welingkar Institute of Management Development and Research Maharashtra Mumbai India

ISBN: (纸本)9798350300093

the last decade has seen an explosion in the use of online social networks (OSNs), with many people relying on them for the majority of their social contacts. they use OSNs to maintain relationships, disseminate information, plan events, and even do business online. Attackers and imposters looking to steal personal information, distribute fake news, or engage in other forms of bad behavior have taken notice of the explosive expansion of OSNs and the wealth of information about its members. However, academics have begun exploring effective approaches to identify suspicious or fraudulent accounts by analyzing account data and applying classification algorithms. In addition, results from stand-alone categorization algorithms aren't always accurate, and some of the exploited account attributes actually have a detrimental impact on the final conclusions or have no impact at all. the solution provided makes use of preprocessing methods like tokenization and the removal of stop words. Chi-squared features are used to select and extract features before an LS TM-ELM model is trained. the proposed method outperforms the previous two models significantly. © 2023 IEEE.

关键词： Social networking (online)

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Joint scheduling of permutation flow shop production and preventive maintenance using a reinforcement learning algorithm 6

Joint scheduling of permutation flow shop production and pre...

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6th international conference on computer Information Science and application Technology, CISAT 2023

作者： Zhenpeng, Ge Jianyou, Xu Hongfeng, Wang College of Information Science and Engineering Northeastern University Shenyang110819 China

ISBN: (纸本)9781510668546

this paper studies a permutation flow shop scheduling problem (PFSSP) considering maintenance activities and energy consumption, in which machine failures obey Weibull distribution. In case of an unexpected failure, a corrective maintenance (CM) policy is triggered automatically. Moreover, preventive maintenance (PM) as a proactive maintenance strategy is introduced to improve deteriorating effects and reduce the probability of failures. then, a multi-objective production-maintenance joint optimization model is proposed. Compared to the traditional PFSSP, this joint model is more complex. To solve it, a reinforcement learning (RL)-based solution approach is designed with specific state sets, action sets and reward functions. Numerical studies show that the application of RL in integrated scheduling has high solution performance by comparing with seven heuristic rules. © 2023 SPIE.

关键词： Energy utilization

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4th international conference on Applied Technologies, ICAT 2022

4th International Conference on Applied Technologies, ICAT 2...

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4th international conference on Applied Technologies, ICAT 2022

ISBN: (纸本)9783031249778

the proceedings contain 113 papers. the special focus in this conference is on Applied Technologies. the topics include: Automatic Evaluation of Physiotherapy Activities Using Deep learning Techniques;multivariable Control Approach Applied to the Embryo Incubation Process of Gallus Gallus Domesticus;discovering Visual Deficiencies in Pilots Candidates Using Data Mining;an Electronic Equipment for Measuring Color Difference Between Tissues Based on Digital Image Processing and Neural Networks;artificial Intelligence-Based Banana Ripeness Detection;U-Net vs. TransUNet: Performance Comparison in Medical Image Segmentation;access Control through Mask Detection and Estimation of People Capacity in Covered Premises;customer Segmentation in Food Retail Sector: An Approach from Customer Behavior and Product Association Rules;Bibliometric Analysis of Web of Science Database STEM Fields in Engineering and Mathematics. Ecuador’s Case Study;smart Antenna Array for Optimal Electromagnetic Energy Capture;image Processing Method to Estimate Water Quality Parameter;use of the Student Engagement as a Strategy to Optimize Online Education, Applying a Supervised machine learning Model Using Facial Recognition;crime Data Analysis Using machine learning Models;Gene therapy as a Solution to Genetic Diseases through DNA Manipulation;Vulnerability of CAPTCHA Systems Using Bots with computer Vision Abilities;military Leadership in the Ecuadorian Army;analysis of Key Variables for Ecuadorian Defense Industry Development;the Prioritization of External Security as a Means of Guaranteeing Multidimensional Security and Economic Growth;Comparative Study of Deep learning Algorithms in the Detection of Phishing Attacks Based on HTML and Text Obtained from Web Pages;ground Robot for Search and Rescue Management;virtual Training Module for the Production of Rubber Adhesives through the Production of Cyclopentenol.

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Towards trustworthy federated learning: A blockchain-based architecture for auditing, traceability and verification 4

Towards trustworthy federated learning: A blockchain-based a...

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4th international conference on computer Science and Communication Technology, ICCSCT 2023

作者： Gao, Hongmin Pan, Xiaofeng Zhang, Xiaojing Ye, Keke Zhang, Peng Sun, Long China Mobile Information Technology Co. Ltd Beijing100032 China China Mobile Information Technology Co. Ltd Guangdong Shenzhen518048 China School of Software Beihang University Beijing100191 China

ISBN: (纸本)9781510671232

the 14th Five-Year Plan proposes the objective of expediting digital development and constructing a digital China, aiming to minimize the reliance on physical travel while fostering increased data flow and sharing. However, the implementation of data privacy regulations, such as the Personal Information Protection Law, has resulted in conflicts between data sharing and privacy. To address this challenge, federated learning technology has emerged as an effective solution that enables data sharing while safeguarding privacy. Nonetheless, trust issues arise among federated members in relation to each other and central servers. this is where blockchain technology, with its notable features of verifiability, auditability, and traceability, can play a pivotal role in resolving trust concerns within federated learning. Consequently, a novel framework called trusted federated learning based on blockchain has emerged. this article initially defines the trust issue in federated learning and subsequently provides a chronological overview of research advancements in the domain of trusted federated learning based on blockchain. Furthermore, it discusses the advantages and disadvantages of existing research across three categories: blockchain structure design, consensus mechanism, and smart contracts. Additionally, the article highlights the application characteristics of this framework in fields such as the Internet of things, connected vehicles, and medical services. Finally, the core challenges and future research directions of trusted federated learning based on blockchain are outlined. © 2023 SPIE.

关键词： Blockchain

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On Campus Student Recruitment Analysis using machine learning techniques 9

On Campus Student Recruitment Analysis using Machine Learnin...

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9th IEEE international conference on Electronics, Computing and Communication Technologies, CONECCT 2023

作者： Varsha, T. Shekar, Yamuna C. Shankar, Sahana P. Bharadwaj, Aryan Varadam, Deepak Supriya, M.S. Ramaiah University of Applied Sciences Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350334395

A nonprofit organization like college, works to assist all student needs and advance science, particularly in the academic sector. Hence, offering a high-quality placement aid service has emerged as a key indicator of a college's overall quality. Nowadays, in this pandemic-affected world, the majority of IT organizations are hiring students online, which makes it difficult for the college and the company to shortlist the students based on the requirements of the companies for various tasks. Due to the large number of students at colleges, the recruitment process requires a lot of human labor. We, therefore, offer a novel method for assessing job applications in online recruitment systems by using several machine learning methods to address the issue. We attempt to put into practice an application in the form of a prototype system, whose functionality is displayed and accessed in a genuine recruitment scenario. It is possible to offer a system where a computer would examine both the company's needs and the student's performance to determine whether the student should be selected for an *** analysis of student recruitment is automated in this paper utilizing machine learning methods by comparing the scores of a test with the benchmark score of the company. © 2023 IEEE.

关键词： Students

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4th Congress on Intelligent Systems, CIS 2023

4th Congress on Intelligent Systems, CIS 2023

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4th Congress on Intelligent Systems, CIS 2023

ISBN: (纸本)9789819990399

the proceedings contain 35 papers. the special focus in this conference is on Intelligent Systems. the topics include: Track learning Agent Using Multi-objective Reinforcement learning;performance Assessment of Gaussian Filter-Based Image Fusion Algorithm;a Cognitive Comparative Analysis of Geometric Shape-Based Cryptosystem;a Lesion Feature Engineering Technique Based on Gaussian Mixture Model to Detect Cervical Cancer;energy Optimization of Electronic Vehicle Using Blockchain Method;pattern Recognition: An Outline of Literature Review that Taps into machine learning to Achieve Sustainable Development Goals;novel Approach for Stock Prediction Using Technical Analysis and Sentiment Analysis;visualizing and Exploring the Dynamics of Optimization via Circular Swap Mutations in Constraint-Based Problem Spaces;An Approach to Increase the Lifetime of Traditional LEACH Protocol Using CHME-LEACH and CHP-LEACH;denseMammoNet: An Approach for Breast Cancer Classification in Mammograms;aspect-Based Sentiment Classification Using Supervised Classifiers and Polarity Prediction Using Sentiment Analyzer for Mobile Phone Tweets;microbial Metabolites and Recent Advancement;design of a 3D-Printed Accessible and Affordable Robotic Arm and a User-Friendly Graphical User Interface;profit Maximization of a Wind-Integrated System by V2G Method;detection of Partially Occluded Area in Images Using Image Segmentation Technique;application of IP Network Modeling Platforms for Cyber-Attack Research;enhancing Information Integrity: machine learning Methods for Fake News Detection;Optimum Selection of Virtual machine in Cloud Using Improved ACO;data Imputation Using Artificial Neural Network for a Reservoir System;depth Multi-modal Integration of Image and Clinical Data Using Fusion of Decision Method for Enhanced Kidney Disease Prediction in Medical Cloud;an Efficient Prediction of Obstructive Sleep Apnea Using Hybrid Convolutional Neural Network.

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4th Congress on Intelligent Systems, CIS 2023

4th Congress on Intelligent Systems, CIS 2023

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4th Congress on Intelligent Systems, CIS 2023

ISBN: (纸本)9789819990368

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Defending the Treasury: Deep learning Strategies for Banking Security Against Trojan threats 4

Defending the Treasury: Deep Learning Strategies for Banking...

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4th international conference on Technological Advancements in Computational Sciences, ICTACS 2024

作者： Kumari, Ankita Gupta, Deepali Uppal, Mudita Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (纸本)9798350387490

Trojans can be a serious threat to today's digital banking: they infiltrate banking systems and compromise delicate financial data. Trojans can cunningly come in through seemingly innocuous channels like email attachments or underserved websites. they use vulnerabilities to enter banking networks without permission. Once inside, they can make fraudulent transactions, steal confidential information such as login credentials and account details, and even cause bank system breakdowns. Trojans bring far-reaching consequences for banking institutions the result is financial losses, damage to reputation and the loss of customer trust. therefore, protection against Trojan threats is a top priority for banks to ensure that their systems are secure and reliable and their customers' assets remain safe from harm. the proposed model delivers a new proactively, not just defense strategy against incidents. Now the AI-enabled chip stands at the core of this defense, using advanced machine learning algorithms to parse input data streams, look for abnormal patterns occurring in them as may herald Trojan activity. By continuously examining network traffic and the users' TS data, the system can quickly find and kill Trojan hazards before they mushroom into a security blowout. the application of Recurrent Neural Network (RNN) in kinetic phase identification of defence bank systems results in a success rate 89%. Leveraging RNN algorithms, this study scrutinized the effects of different trojan detections with surprising results. the findings further suggest that RNN-based strategies can greatly improve banking security and also reduce potential risks for trojan malware. By using advanced machine learning methods such as RNN, banks can protect their sensitive financial assets not only against future cyber attacks but in addition turn out service wreckage into individual opportunities which they manage for profit. © 2024 IEEE.

关键词： Recurrent neural networks

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2023 12th international conference on Modern Circuits and Systems Technologies, MOCAST 2023 - proceedings

2023 12th International Conference on Modern Circuits and Sy...

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12th international conference on Modern Circuits and Systems Technologies, MOCAST 2023

ISBN: (纸本)9798350321074

the proceedings contain 104 papers. the topics discussed include: novel environmental magnetic field measurement using a drone;research and practice of automatic identification method for construction materials based on deep learning;synthesis of ultra-wideband rectenna for RF energy harvesting from wireless communications networks;machine learning-based surrogate modelling of reflectarray unit cell in a 4-D parallelotope-shaped domain;modified pre-processing stage for improving the noise immunity of the Pan-Tompkins algorithm;cellular nonlinear network circuit model with application to seizures prediction;on the additive manufacturing of conformal slotted waveguide antennas;programmable metasurface-based beam forming by physics-driven deep learning network;a vision-based deep learning platform for human motor activity recognition;and design and simulation of a classic controller to reduce undesired coupling axial and torsional vibrations in a horizontal drill string.

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