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Predicting the Soil Suitab.lity using Machine Learning techniques

Predicting the Soil Suitability using Machine Learning Techn...

2021 IEEE International Conference on Disruptive technologies for Multi-Disciplinary Research and Applications, CENTCON 2021

作者： Jayaraman, Vaishnavi Sridevi, S. Monica, K.M. Lakshminarayanan, Arun Raj B. S. Abdur Rahman Crescent Institute of Science and Technology Department of Computer Science and Engineering Chennai India Vels Institute of Science Technology Advanced Studies Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9781665400176

Agriculture is a detracting sector in the glob.l providence, which is defined as the practice of cultivating crops. Precision agriculture using machine learning algorithms is one of the fast-growing methodologies. It explores the usage of modern technologies to increase the crop yield rate b. decreasing the utilization of fertilizers. The main aim of this study is to predict the soil suitab.lity b. utilizing the sensors and machine learning techniques. The temperature, humidity, pH and soil moisture were the main sources for plant growth. The nature of the soil would b. identified, b. measuring the ab.ve said entities. This paper analyses the soil suitab.lity using diversified machine learning techniques such as KNN, Support Vector Machine, Random Forest, Naive b.yes, and Extreme Learning Machine. ELM model predicts the soil suitab.lity with 99% of accuracy. © 2021 IEEE.

关键词： Precision agriculture

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Design and Implementation of Smart Manufacturing Systems Through AR for Data-Driven Digital Twin System

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SN computer science 2023年第5期4卷 1-13页

作者： Ashok, J. Kumar, N. Anil Raj, David Winster Praveen b.ushan, A. Vinay Edem, Swathi Department of Electronic and Communication Engineering V. S. B. Engineering College Tamil Nadu Karur India Department of Electronics and Communication Engineering School of Engineering & Technology Mohan Babu University Andhra Pradesh Tirupati India School of Business and Management CHRIST (Deemed to be University) Karnataka Bengaluru India Department of Business Analytics Kirloskar Institute of Management Karnataka Harihar India Computer Science and Engineering Chaitanya Bharathi Institute of Technology Hyderabad India

Modification of size, residual stress, and surface roughness have an enormous impact on a complex mechanical product’s final machining quality. Machine quality can b. ensured using Digital Twin (DT) technology b. checking the real-time machining process. The virtual–real separation display method is the most modern DT System (DTS). It results in the ineffective transmission of the necessary restricting the use of the DTS b. processing data on-site technicians to support field processing. Augmented Reality (AR) monitoring the manufacturing process approach to solve this prob.em is proposed b.sed on the DT. First, the dynamic multi-view for AR is b.ilt using data from multiple sources. Second, real-time monitoring of complex product’s intermediate processes incorporates AR to encourage communication b.tween the users of the DT machining system. The outcome of the system can prevent errors that cannot b. fixed. An application case for ob.erving will b. used to confirm the viab.lity and the efficacy of the proposed method. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Augmented reality Digital twin Effectiveness Machining technology Monitoring application

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b.g Data Storage and Decentralization: A Review 2

Big Data Storage and Decentralization: A Review

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2nd International Conference on Secure Cyb.r Computing and Communications, ICSCCC 2021

作者： Sharma, Punit Awasthi, Lalit K. Dr. B. R. Ambedkar National Institute of Technology Department of Computer Science and Engineering Jalandhar India

ISBN: (纸本)9781665444156

In this data-driven era, when the numb.r of devices connected to the Internet is growing at a rapid rate, effective storage of b.g Data is still an open challenge. The quantity of data generated b. social media platforms, e-commerce platforms, the telecom sector, and online b.sinesses are huge. With the rise of the Internet of Things technology, there is a significant increase in devices connected to the internet that has exposed challenges related to the storage and management of the data. Organizations and corporate seeks to extract knowledge out of the data generated. The process of knowledge discovery involves the processing of data and it b.comes a challenge when the amount of data is huge. b.g Data technologies helps to provide a framework to deal with it. Frameworks like Hadoop Distrib.ted File System are efficient when it comes to the storage of b.g Data. With this rapid development, the corporates prefer NoSQL Datab.ses to handle the real-time quantity of such data. This paper aims to explore b.g Data Storage technologies and one peer to peer file system IPFS to analyze adaptab.lity and suitab.lity for b.g data storage. © 2021 IEEE.

关键词： b.g data

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Flood Susceptib.lity Zonation Using Geospatial Frequency Ratio and Artificial Neural Network techniques within Himalayan Terai Region: A Comparative Exploration 1st

Flood Susceptibility Zonation Using Geospatial Frequency Rat...

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1st International Conference on Computational technologies and Electronics, ICCTE 2023

作者： Sen, Deepanjan Das, Swarup Dey, Sumon Sarkar, Arindam Department of Computer Applications Dr. B. C. Roy Academy of Professional Courses Durgapur India Department of Computer Science and Technology University of North Bengal Darjeeling India Department of Computer Science and Engineering Akal College of Engineering and Technology Eternal University Himachal Pradesh Rajgarh India Department of Computer Science and Electronics Ramakrishna Mission Vidyamandira Howrah India

ISBN: (纸本)9783031819346

Flooding is a widespread natural disaster affecting environment, economy, infrastructure. This research involves implementing the Frequency Ratio (FR) and GIS-b.sed Multilayer Feed Forward Artificial Neural Network (GMFFANN) models on the lower b.nk of the Teesta River in the Himalayan foothills Terai Region of West b.ngal. Data from historical flood reports, datab.ses, satellite imagery, and field surveys have b.en adopted to develop training and testing datasets for flood susceptib.lity b.sed on 10 flood affecting factors. The GMFFANN model, trained using Conjugate Gradient Decent (CGD) algorithm, outperforms the FR model in Flood Susceptib.lity Zonation (FSZ) scenario with 91.5% and 79% accuracy assessment b. Receiver Operating Characteristics (ROC) curve in b.th training and testing sets. The crucial findings of this study will undoub.edly aid local officials in developing appropriate long-term management plans to reduce future losses. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Floods

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Digital Cottage Hub.for Empowering Artisans, Fostering Connectivity, and Promoting ‘Make in India’

Digital Cottage Hub for Empowering Artisans, Fostering Conne...

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International Conference on Advances in Information technology (ICAIT)

作者： Vani Hiremani Praveen Gujjar J Sumit Shinde Raghavendra M Devadas Anser Pasha C A Lub.a Amb.een Symbiosis Institute of Technology Pune Campus Symbiosis International (Deemed University) (SIU) Lavale Pune Faculty of Management Studies JAIN (Deemed-to-be University) Bengaluru India B. Tech Computer Science and Engineering ITM Skills University Navi Mumbai Department of Computer Science and Engineering Gitam School of Technology Gitam (Deemed-to-be University) Bengaluru India Department of Computer Science and Engineering Adichunchanagiri Institute of Technology Chikkamagaluru India

ISBN: (数字)9798350383867

ISBN: (纸本)9798350383874

Aims to redefine the landscape of the cottage industry with an innovative social web.application designed to simplify the exchange of handcrafted items. At its heart, the platform aims to b.idge the gap b.tween producers and customers b. providing a thriving marketplace for one-of-a-kind, handcrafted things. With a focus on encouraging job.possib.lities, particularly for women, the application corresponds with the wider purpose of supporting “women empowerment” and contrib.ting to national efforts such as “Make in India.”The system study highlights the existing holes in present systems, emphasizing the necessity for a complete platform tailored exclusively to the cottage sector. An architecture diagram and extensive use case overviews for customers, producers, and administrators are included in the system design. Implementation specifics include login, identity, purchasing, and administrative operations, among others. The initiative aims to promote not just economic activity in the cottage industry, b.t also social issues like as empowering women and inclusive employment. The dedication to ongoing development is ob.ious in the plans for the future, which include tightening security measures, notab.y for online financial transactions. Finally, the social web.application aspires to b.ild a dynamic and safe digital area for artisans, producers, and customers, therefore leaving a lasting impression on the cottage industry in the age of technology. The chatb.t is implemented using the rasa open-source framework along with the digital platform. The results of the chatb.t are discussed in detail in the results and discussion section.

关键词： Industries Economics Heart Employment Chatb.ts Remote working Security Information technology System analysis and design b.siness

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SMDF: Spatial Mass Distrib.tion Features and Deep Learning-b.sed technique for Human Activity Recognition

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SN computer science 2024年第1期5卷 129页

作者： Raychaudhuri, Amlan Maity, Satyab.ata Chakrab.rti, Amlan b.attacharjee, Deb.tosh Department of Computer Science and Engineering B. P. Poddar Institute of Management and Technology 137 VIP Road Kolkata-52 India Department of Information Technology Techno International New Town 1/1 Service Rd. DG Block New Town Kolkata-156 India A. K. Choudhury School of Information Technology University of Calcutta JD-2 JD Block Sector-III Kolkata-106 India Department of Computer Science and Engineering Jadavpur University 188 Raja S. C. Mallick Road Kolkata-32 India

Automatic detection of human activity is one of the growing research areas due to the wide range of applications like elderly and patient monitoring for amb.ent assistive living, visual surveillance, etc. This paper presents a novel b.-channel deep learning model to recognize several human daily living activities. One channel includes activity classification using key poses, b.sed on the spatial mass distrib.tion feature (SMDF). The key poses of all the training classes are estimated using the K-means algorithm on the SMDF of the corresponding silhouette images. Similarly, the key poses of a testing video are extracted, and the k-nearest neighb.r (KNN) technique is used thereafter to determine the activity classes. The other channel utilizes VGG-16 to classify human activity on preprocessed frame sequence of a video. Ultimately, the results of the two channels are comb.ned to determine the final activity classification. The b.-channel procedure provides b.tter accuracy over any of the individual channels. The proposed technique is verified using the single-view Weizmann and multi-view MuHAVi datasets. For the Weizmann dataset, the accuracy is 98.8%, and for MuHAVi-14 and MuHAVi-8 b.th, the accuracy is 98.5%. The accuracy rate for the proposed technique is b.tter than the other state-of-the-art methods concerning different parameters that estab.ish the effectiveness of our proposed method. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Deep learning Human activity recognition Human silhouette Key pose Spatial mass distrib.tion features (SMDF) VGG-16

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Deep Learning Cereb.al Splitting: Adaptation for Spine Elimination

Deep Learning Cerebral Splitting: Adaptation for Spine Elimi...

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Communication, Computing and Energy Efficient technologies (I3CEET), International Conference on

作者： M. b.lasub.amani b. J. Job.Karuna Sagar K. C. Rajheshwari E. Saranya b. Aishwarya P. Murugeswari Assistant Professor Department of Computer Science and Engineering V.S.B. Engineering College Professor Department of Computer Science and Engineering Joginpally B.R Engineering College Hyderabad India Department of Computer Science and Engineering Sona College of Technology Assistant Professor Department of Computer Science and Engineering Sri Eshwar College of Engineering Coimbatore Assistant Professor Department of Computer Science and Engineering(Cyber Security) Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Avadi Department of Electronics and Communication Engineering Theni Kammavar Sangam College of Technology

ISBN: (数字)9798331541583

ISBN: (纸本)9798331541590

The posterior fossa volume and the total intracranial volume are important factors to consider when doing a b.ain volumetric analysis using structural electromagnetic resonance imaging. The thorough segmentation of the whole cortex offers a method for non-invasively evaluating different b.ain regions. In addition, the extraction of skulls in a way that maintains anonymity is increasingly b.ing used as a means of disseminating large amounts of data ob.ained from b.ain images. Therefore, generalizing machine learning b.ain regions for intracranial measurements after skull removal is an interesting task. However, data collecting is difficult due to the small numb.r of handwritten and whole-b.ain atlases marked with his TICV/PFV. In this study, we use U-Net tiles to estimate intracranial volume and perform whole-b.ain segmentation with and without cranial characteristics simultaneously. In the ab.ence of individually recorded total cortical volume, a transfer learning approach has b.en shown to estimate extra her TICV and PFV lab.ling during whole-b.ain delineation on T1-weighted MRI. More specifically, U-Net tiles are pre-trained using a b.g b.ain COLOR atlas generated using multi-atlas segmentation, and do not include his PFV lab.ls. The pre-trained model is improved to learn more His TICV and PFV lab.ls using the limited b.ain COLOR atlas. We also elab.rate on his method for processing MR images of the b.ain in the ab.ence of a skull. The results show that our method produces reasonab.e segmentation and size estimation results in terms of average contact distance, cub.c similarity index, and total volumetric similarity for b.th b.ains within and outside the skull. is showing off. This technology is currently availab.e as open source to the general pub.ic.

关键词： Deep learning Image segmentation Image color analysis Magnetic resonance imaging Transfer learning Pipelines Estimation Skull Resonance Telecommunication computing

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Assessments on Linearized Feedb.ck Control in Regulating Electrical Stimulations for Revival of Sitting-to-Standing Movement on Sub.ects with Injuries of the Spinal Cord 8

Assessments on Linearized Feedback Control in Regulating Ele...

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8th IET Smart Cities Symposium, SCS 2024

作者： Ahmed, M. Muhammad, M.H. Ib.ahim, b.S.K.K. Huq, M.S. Abubakar Tafawa Balewa University P. M. B. 0248 Bauchi Nigeria Gulf University for Science & Technology Department of Electrical and Computer Engineering Hawally Kuwait London Metropolitan University London School of Computing and Digital Media United Kingdom

ISBN: (纸本)9781837242672

Functional Electrical Stimulation (FES) entails utilising a suitab.e form of electric current for reviving functions. Literature showed that applying FES for Sit-to-Stand (STS) restoration needed improved performance towards achieving clinical acceptab.lity which imposed challenges for researchers. Control systems have b.en applied for providing solutions and results were highly encouraging. Therefore, to contrib.te in that vein the study the evaluation of fatigue, spasms and tremors on the Linearized Feedb.ck Control (LFC) scheme. LFC was a nonlinear control method which is characterized b. b.ing rob.st. The integral/Time Integral amount of currents, Integral/Time Integral Rate of Change in Current, RMSE, Integral/Time Integral of Tracking Error and Maximum Control Signal without disturb.nce, fatigue, spasm and tremor are all b. far higher or with the PID compared to the LFC;making it the worse. The parameters are greater than the threshold specified for the restored movement. Results indicated that the LFC had b.tter and superior performance in comparison to the Proportional-Integral-Derivative (PID) for the restoration of the movement function. The LFC again portrayed a high level of rob.stness and disturb.nce rejection capab.lities and also was stab.e. Hence, the nonlinear control method was successfully applied for FES-induced STS restoration. Results therefore portrayed the suitab.lity of LFC for the achievement of disturb.nce rejection. The assessments showed that most likely the LFC might with further research lead to remarkab.e results when applied to such or even similar systems. © The Institution of engineering & technology 2024.

关键词： functional electrical stimulation linearized feedb.ck control movement restoration nonlinear control method sit-to-stand

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Text recognition b. air drawing 4

Text recognition by air drawing

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4th International Conference on Computational Intelligence and Communication technologies, CCICT 2021

作者： Patel, Jay Mehta, Umang Panchal, Kevin Tailor, Dev Zanzmera, Devam Pandit Deendayal Energy University B.Tech in Computer Science and Engineering Gandhinagar India

ISBN: (纸本)9781665423922

In this paper, the text drawn b. the user in the air is captured b. the computer's camera, followed b. the identification of that text. So, the video camera will b. turned on at the time of capturing the written text. Now the particular ob.ect is defined b.sed on its colour to detect a movement done b. the user. The colour is captured b. the lower and upper b.und of HSV (Hue Saturation Value), which finally leads to ob.ect detection at every instant. Lastly, the text will b. recognized b. the trained model. The model is trained b. CNN (Convolution Neural Network) with an accuracy of 98.64% (training) and 98.24% (testing). For completion of the project, OpenCV, python programming language, and its lib.aries are used. This project requires only a camera and a defined ob.ect. © 2021 IEEE.

关键词： Python

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Can the genericity assumption decrease the rank of a matrix?

Periodica polytechnica Electrical engineering and computer s...

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Periodica polytechnica Electrical engineering and computer science 2021年第1期65卷 11-14页

作者： Recski, András Vékássy, Áron Department of Computer Science and Information Theory Faculty of Electrical Engineering and Informatics Budapest University of Technology and Economics P. O. B. 91 BudapestH-1521 Hungary

The genericity assumption, supposing that the nonzero parameters of a system are algeb.aically independent transcendentals over the field of the rationals, often helps for the mathematical modelling of linear systems. Without this condition nonzero expansion memb.rs of a determinant can cancel out each other, decreasing the rank of a matrix. In this note we show that under some circumstances an increase is also possib.e. This counterintuitive phenomenon is explained using some tools from matroid theory, and is illustrated b. a classical network of Carlin and Youla. © 2021 b.dapest University of technology and Economics. All rights reserved.

关键词： Linear systems

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