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Information Technology and Digital Applications (ICITDA), IEEE International Conference on

作者： Hushalictmy Paliyanny Rajermani Thinakaran Somasekar Jalari Vikram Neerugatti Madhusudana Rao Nalluri Ravindra Raman Cholla Faculty of Data Science and Information Technology INTI International University Malaysia Department of Computer Science and Engineering Faculty of Engineering and technology Jain (Deemed-to-be University) Bangalore India Department of Computer Science and Engineering ICFAI Foundation for Higher Education Hyderabad Telangana India

ISBN: (数字)9798331510732

ISBN: (纸本)9798331510749

Internet of Things plays an important role in agriculture in order to provide an innovative and smart solution to traditional farming. IOT is all about connecting physical devices to the internet and can access from a remote place. So that crop monitoring becomes easy even if the farmer is in a remote place. Due to automation, human efforts are required less. Also the IoT data helps the farmer make decisions about planting, harvesting or fertilizing. This paper explains the system we have implemented for monitoring the agriculture with the help of IoT. The system is developed in order to provide real time information on crop conditions to the farmer. For that, it uses various types of sensors connected to the microcontroller in order to measure parameters like temperature, soil moisture, and water level, and accordingly, it activates actuators like a fan, motor or pump to manage ventilation and irrigation. We have used the GSM module to send the alert to the farmer and the WiFi module is used to send the agriculture data to the cloud platform. So that farmer can monitor the farm or crops remotely. The system is continuously collecting farm data from the sensors. If the crop soil is very dry them system will turn on the water pump. Also, for high temperature, the fan will get activated in order to cool down that area. This feature is mostly used in indoor farming. The farmer can monitor the farm condition in real time with the help of cloud platform. This system is helpful for improving productivity, crop management, and save resources i.e. efficient use of water which prevents water wastage.

关键词： Temperature sensors Temperature measurement Fans Soil moisture Crops Real-time systems Water pumps Water resources Monitoring Farming

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The Effect of Cache Decision Policies on QoE in Multiple Video and Audio Streaming over ICN/CCN

The Effect of Cache Decision Policies on QoE in Multiple Vid...

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Communications and Networking (MeditCom), IEEE International Mediterranean Conference on

作者： Keisuke Kobayashi Toshiro Nunome Department of Engineering Computer Science Program Graduate School of Engineering Nagoya Institute of Technology Nagoya Japan

ISBN: (数字)9798350309485

ISBN: (纸本)9798350309492

This paper considers video and audio transmission in ICN (Information-Centric Networking)/ CCN (ContentCentric Networking), where each intermediate node can cache content. We have proposed LCD with Prob, combining traditional policies: LCD (Leave Copy Down) and probabilistic caching. This paper assumes a case where multiple video and audio streams exist simultaneously. We assess application-level QoS (Quality of Service) and QoE (Quality of Experience) of video and audio transmission utilizing LCD with Prob. We also add a mechanism for further QoE enhancement called “LCD with Prob deletion.” We assess application-level QoS using a computer simulation with a tree network and QoE by means of a subjective experiment. From the assessment result, we show the effectiveness of LCD with Prob deletion against existing policies.

关键词： Network topology Scalability Redundancy Quality of service Streaming media Probabilistic logic Liquid crystal displays

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Tile selection method based on error minimization for photomosaic image creation

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Frontiers of computer science 2021年第3期15卷 165-172页

作者： Hongbo ZHANG Xin GAO Jixiang DU Qing LEI Lijie YANG Department of Computer Science and Technology Huaqiao UniversityXiamen 361021China Fujian Key Laboratory of Big Data Intelligence and Security Huaqiao UniversityXiamen 361021China Xiamen Key Laboratory of Computer Vision and Pattern Recognition Huaqiao UniversityXiamen 361021China School of Computer Science and Technology Harbin Institute of TechnologyShenzhen 518055China

Photomosaic images are composite images composed of many small images called *** its overall visual effect,a photomosaic image is similar to the target image,and photomosaics are also called“montage art”.Noisy blocks and the loss of local information are the major obstacles in most methods or programs that create photomosaic *** solve these problems and generate a photomosaic image in this study,we propose a tile selection method based on error minimization.A photomosaic image can be generated by partitioning the target image in a rectangular pattern,selecting appropriate tile images,and then adding them with a weight *** on the principles of montage art,the quality of the generated photomosaic image can be evaluated by both global and local *** the proposed framework,via an error function analysis,the results show that selecting a tile image using a global minimum distance minimizes both the global error and the local error ***,the weight coefficient of the image superposition can be used to adjust the ratio of the global and local ***,to verify the proposed method,we built a new photomosaic creation dataset during this *** experimental results show that the proposed method achieves a low mean absolute error and that the generated photomosaic images have a more artistic effect than do the existing approaches.

关键词： photomosaic image tile image target image error minimization mean absolute error

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Text Categorization of Job Vacancy Using Recurrent Neural Network Method

Text Categorization of Job Vacancy Using Recurrent Neural Ne...

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2021 International Conference on Artificial Intelligence and Big data Analytics, ICAIBDA 2021

作者： Azzahra, Sarah Hasna Nurma Yulita, Intan Hidayat, Akik Universitas Padjadjaran Department of Computer Science Bandung Indonesia Universitas Padjadjaran Research Center for AI and Big Data Department of Computer Science Bandung Indonesia

ISBN: (纸本)9781665408905

The need for information on job vacancies will increase over time. This results in information on job vacancies being updated very quickly and the data collected will be more numerous and varied. One way to group information is to tag each job vacancy. It can be possible that a job vacancy contains the characteristics of more than one category or is commonly referred to as multilabel This research aims to categorize job vacancies so that job vacancies can be grouped into appropriate categories automatically. The categories chosen to be categorized are Administration, Finance, IT, and Marketing. This research uses the Recurrent Neural Network method. In obtaining the optimal model, this research tested the hyperparameters. Testing on the Word2vec hyperparameter produces a dimension of 150 and a window size of 15. In the Recurrent Neural Network hyperparameter, the optimal model is obtained when the number of units in the Recurrent Neural Network is 200, the batch size is 32, and the threshold is 0.2. This research resulted in a Hamming score of 83.07% and an F1 score of 86.15%. © 2021 IEEE.

关键词： Recurrent neural networks

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Control of Magnetic Skyrmions in an Exchange Biased van der Waals Ferromagnet

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Microscopy and microanalysis : the official journal of Microscopy Society of America, Microbeam Analysis Society, Microscopical Society of Canada 2023年第1期29卷 1710-1711页

作者： McCray, Arthur R C Lebedev, Dmitry Arpaci, Sevdenur Te Velthuis, Suzanne G E Lopez-Dominguez, Victor Amiri, Pedram Khalili Hersam, Mark C. Petford-Long, Amanda K. Phatak, Charudatta Materials Science Division Argonne National Laboratory Lemont IL United States Applied Physics Program Northwestern University Evanston IL United States Department of Materials Science and Engineering Northwestern University Evanston IL United States Department of Electrical and Computer Engineering Northwestern University Evanston IL United States

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Boruta Feature Selection for Prediction of Coronary Artery Disease

Boruta Feature Selection for Prediction of Coronary Artery D...

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Research Methodologies in Knowledge Management, Artificial Intelligence and Telecommunication Engineering (RMKMATE), International Conference on

作者： S. Yuvalatha S. Nithyapriya R. Tamizh Kuzhali S. Savitha Department of Computer Science and Business Systems Bannari Amman Institute of Technology Sathyamangalam Department of Artificial Intelligence and Data Science Bannari Amman Institute of Technology Sathyamangalam Department of Artificial Intelligence and Data Science C K College of Engineering and Technology Chennai Department of Computer Science and Engineering PSR Engineering College Sivakasi

Day by day cardio vascular disease death cases increasing. It mainly affects the human heart and blood vessels and it is difficult to diagnosis it. In this paper various machine learning algorithm is used to predict the cardio vascular disease. The data set of 14 attributes is used for prediction. The irrelevant features are handled using Boruta algorithm of 100 iterations. The proposed work uses Adaboosting with different hyper parameter and the multiple decision trees are built for the wrongly classified feature. At finally the Adaboosting algorithm achieve 93.74% accuracy in predicting the disease.

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Personalized Method for Voting in Wireless Mobile Adhoc Networks with Intrusion Detection using NS2 Simulation Model

Personalized Method for Voting in Wireless Mobile Adhoc Netw...

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Smart Electronics and Communication (ICOSEC), International Conference on

作者： S Muthukumar S Senthil Pandi D Menaga T.N. Sudhahar Department of Computer Science and Engineering St.Joseph’s Institute of Technology Chennai Tamilnadu India Department of Computer Science and Engineering Rajalakshmi Engineering College Chennai Tamilnadu India Department of Artificial Intelligence and Data Science Rajalakshmi Institute of Technology Chennai Tamilnadu India

ISBN: (数字)9798331504403

ISBN: (纸本)9798331504410

Mobile Ad hoc Networks (MANETs) are wireless networks that are self-configuring, infrastructure-less, and dynamic. The nodes in these networks have limited access to available resources. MANETs use intrusion detection systems, also known as IDS, to monitor activities in order to identify any potential intrusions into the network, which would otherwise be vulnerable. In this study, we offer efficient techniques for maximizing the length of time that intrusion detection systems need to be active in a mobile ad hoc network and monitoring how much time they need to be active for. The dissemination of a probabilistic model that makes use of neighborhood node IDS collaboration in order to reduce the amount of individual active time. In order to monitor network activities, an IDS often needs to be running nonstop on every node. In terms of electricity and processing resources, this could end up being a costly overhead for a mobile device that runs on batteries. We therefore want to shorten the IDSs’ active time in this work without sacrificing their effectiveness. We simulate the interactions between IDSs as a multiplayer cooperative game with partially cooperative and partially opposing aims in order to validate our suggested methodology. We investigate this game theoretically and provide evidence from simulations.

关键词： Wireless networks Simulation Intrusion detection Games Software Security Monitoring Mobile computing Load modeling Mobile ad hoc networks

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Are Fair Learning to Rank Models Really Fair? An Analysis Using Inferred Gender

Are Fair Learning to Rank Models Really Fair? An Analysis Us...

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2022 IEEE MIT Undergraduate Research Technology Conference, URTC 2022

作者： Pietrick, Alexander Romportl, Alyssa Smith, Shailen Olulana, Oluseun Cachel, Kathleen Rundensteiner, Elke Worcester Polytechnic Institute Departments of Computer Science and Data Science WorcesterMA United States St. Olaf College Departments of Mathematics and Psychology NorthfieldMN United States Stony Brook University Department of Mathematics Stony BrookNY United States Worcester Polytechnic Institute Department of Data Science WorcesterMA United States

ISBN: (纸本)9781665473453

Fair Learning To Rank (LTR) frameworks require demographic information;however, that information is often unavailable. Inference algorithms may infer the missing demographic information to supply to the fair LTR model. In this study, we analyze the effect of using a trained fair LTR model with uncertain demographic inferences. We show that inferred data results in varying levels of fairness and utility depending on inference accuracy. Specifically, less accurate inferred data adversely affects the rankings' fairness, while more accurate inferred data creates fairer rankings. Therefore, we recommend that a careful evaluation of demographic inference algorithms before use is critical. © 2022 IEEE.

关键词： Inference engines

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Dynamic Lane Management with IoT for Real-time Lane Configuration and Traffic Flow 2

Dynamic Lane Management with IoT for Real-time Lane Configur...

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2nd International Conference on computer, Communication and Control, IC4 2024

作者： Pasha, Mohammed Rehaman Latha, S. Usha, R. Marin, V. Bini Ishwarya, M.V. Muthulekshmi, M. Malla Reddy Institute of Engineering and Technology Department of Computer Science and Engineering Telangana Hyderabad India Department of Department of Biotechnology Tamil Nadu Salem India S. A. Engineering College Department of Mathematics Tamil Nadu Chennai India Srm Institute of Science and Technology Faculty of Management Studies Tamil Nadu Ramapuram Chennai India Agni College of Technology Artificial Intelligence and Data Science Department Tamil Nadu Thazhambur India Saveetha University Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Biomedical Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350387933

Traffic may be reduced and transportation efficiency improved by properly controlling traffic flow in urban areas. This research provides a new method for managing lanes in real-time by using IoT connectivity. To optimize traffic flow in response to shifting demand patterns, the suggested system prioritizes making lane design changes in real time depending on the current traffic conditions. Using information collected from Internet of Things (IoT) sensors and linked automobiles, the system may dynamically allot lanes in response to variations in traffic volumes and congestion. It describes developing and deploying a dynamic lane control system based on the Internet of Things, stressing the technology's potential to improve traffic flow in congested metropolitan areas. The simulation results show its efficiency in decreasing traffic and increasing throughput. It demonstrates the potential of intelligent transportation systems for creating smarter and more efficient cities by combining IoT, real-time data processing, and traffic control. © 2024 IEEE.

关键词： Internet of things

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Utilization of Machine Learning Algorithms to Monitor the Growth Path of Fishes in Marine Aquaculture 15th

Utilization of Machine Learning Algorithms to Monitor the Gr...

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15th International Conference on Soft Computing and Pattern Recognition, SoCPaR 2023 and 14th World Congress on Nature and Biologically Inspired Computing, NaBIC 2023

作者： Ajibade, Samuel-Soma M. Essah, Richard Tapales, Catherine P. Antecristo, Charmaine P. Cababat, Feliciana G. Oyebode, Oluwadare Joshua Akintoye, Kayode A. Department of Data Science and Artificial Intelligence Faculty of Engineering and Technology Sunway University Selangor Malaysia Department of Computer and Information Sciences Sunway University Selangor Malaysia Department of Computer Science and Engineering Chandigarh University Punjab India College of Arts and Sciences Cebu Technological University Cebu Moalboal Philippines Cebu Technological University Cebu Moalboal Philippines College of Education Cebu Technological University Cebu Moalboal Philippines Department of Civil and Environmental Engineering ABUAD Ado Ekiti Nigeria Department of Computer Science The Federal Polytechnic Ekiti State Ado Ekiti Nigeria

ISBN: (纸本)9783031810855

Under a realistic cellular metabolic economic model of Nile tilapia, this work investigates Q-learning fish growth trajectory analysis. we offer two Q-learning data mining algorithms the best management policy using simulated data of optimum growth projections starting at the early growth stage and ending at appropriate market share. The early or primitive Q-learning technique learns optimum feed supplying regulatory regime for fish cultivation rate for the fished present the cages, whereas other one continuously adjusts appropriate nutrition controls the law for agricultural fish rate of increase in tanks together within ideal temperature pattern. The cumulative percentage weight of fishes through both vessels on land and afloat cages was tracked with relative dynamic model errors of 1.7% and 6.6%, respectively, using Q-learning control rules. Furthermore, contrasted to drifting cages where its water temp is controlled at 29.7 ℃, the eating and heat management policies minimise the proportional rate of feeding along with wastage of food in by 11%. data, algorithms, and performance of DL approaches used in adaptive aquaculture farms are also examined. In a nutshell, our goal is to give scholars and clinicians a greater grasp of the latest advancements of deep learning in aquaculture, which will help them develop smart aquacultural applications. With improved machine - learning technologies, a video trying to track biological early warning has made significant progress. Artificial intelligence has helped researchers better understand behavioural reactions to pharmacological and environmental stress. We discuss pioneer efforts in exact monitoring of a gathering of individuals in 2d/3d space in this paper, which introduces the fundamentals of activity recognition. Video tracking's professional and operational difficulties are discussed. The toxicological study based on fish behavioral data is then summarized. © The Author(s), under exclusive license to Springer Nature

关键词： Deep learning

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