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Pedometric mapping for soil fertility management – A case study

Journal of the Saudi Society of Agricultural sciences 2021年第2期20卷 128-135页

作者： Leena, H.U. Premasudha, B.G. Panneerselvam, S. Basavaraja, P.K. Department of Master of Computer Applications Siddaganga Institute of Technology and Affiliated to Visvesvaraya Technological University Belagavi B.H. Road Tumakuru 572103 Karnataka India Department of Master of Business Administration Siddaganga Institute of Technology B.H. Road Tumakuru 572103 Karnataka India AICRP on STCR Department of Soil Science & Agricultural Chemistry UAS GKVK Bengaluru 560065 India

Information about soil physico-chemical parameters plays an important role in precision farming. To examine the relationship among soil properties, pedometric mapping is essential and has been widely applied in agricultural activities. This paper is aimed at soil nutrient assessment by generating the geospatial distribution maps for Kestur, a village in Tumakuru district of Karnataka State, India. Using random sampling technique, a total of one hundred and sixty geo-coordinated surface (0–20 cm) soil samples were collected from different land cover regions including irrigated and dryland areas in Kestur village. The samples collected were analysed for pH, electrical conductivity (EC), organic carbon (OC), available nitrogen (N), phosphorus (P as P₂O₅), and potassium (K as K₂O). Also, a structured questionnaire was used to collect data about demographic profile, farming practices, crops grown and fertilizers applied from each farmer under the study. The spatial distribution maps were generated for each soil property and were mapped using spatial variability by adopting Ordinary Kriging geostatistical method. Semivariogram models such as exponential for pH, spherical model for EC and OC and Gaussian for N, P₂O₅, and K₂O were used based on the best-fit selection using least Mean Error (ME) and Root Mean Square Error (RMSE) values. The outcome of the study proved that the geostatistical methods can be utilized to develop the spatial distribution maps thereby resulting in a cost effective solution to soil fertility management. © 2020 The Authors

关键词： Geostatistics Pedometric mapping Soil fertility Soil nutrient assessment

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Deep Learning Techniques for Intrusion Detection Systems in Healthcare Environments

Deep Learning Techniques for Intrusion Detection Systems in ...

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International Conference on Communication Systems and Network Technologies (CSNT)

作者： Mahfooz Alam Zubair Ashraf Priya Singh Bishwajeet Pandey Kaleemur Rehman Laura Aldasheva Department of Master of Computer Applications G. L. Bajaj Institute of Technology and Management Greater Noida India College of Computing and Information Technology University of Bisha Bisha Kingdom of Saudi Arabia Department of Computer Science Institute of Engineering & Technology Dr. B. R. Ambedkar University Khandri Agra India Department of CSE-AI G. L. Bajaj Institute of Technology and Management Greater Noida India Department of IS and CS Astana IT University Astana Kazakhstan

ISBN: (数字)9798331531935

ISBN: (纸本)9798331531942

Nowadays, healthcare systems face ever-increasing cyber security threats due to the sensitive nature of patient data and the proliferation of IoT-enabled medical devices. Traditional Intrusion Detection Systems (IDS) struggle to adapt to healthcare environments' dynamic and heterogeneous data. Deep learning techniques, with their ability to analyze complex and high-dimensional data, have emerged as a promising approach to enhancing IDS performance. This paper explores the application of deep learning methods for intrusion detection in healthcare, focusing on their advantages, challenges, and practical implementations. Topics include neural network architectures, feature engineering for medical and network data, and real-time intrusion detection in resource-constrained environments. The goal is to provide a roadmap for leveraging deep learning to ensure robust and scalable security for healthcare systems.

关键词： Deep learning Performance evaluation Medical devices High dimensional data Neural networks Intrusion detection Medical services Real-time systems Security Long short term memory

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Revolutionizing Plant Disease Management Through Image Processing technology

Revolutionizing Plant Disease Management Through Image Proce...

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Computational Intelligence and Sustainable Engineering Solutions (CISES), International Conference on

作者： Gaurav Kumar Ankit Kumar Manoj Singhal Kamred Udham Singh Lalan Kumar Teekam Singh Department of Computer Engineering & Applications GLA University Mathura UP India Department of Information Technology GL Bajaj Institute of Technology and Management India School of Computing Graphic Era Hill University Dehradun India Department of Master of Computer Application GL Bajaj Institute of Technology and Management India Department of Computer Science and Engineering Graphic Era Deemed to be University Dehradun India

Agricultural productivity plays a vital role in the economy, and disease detection in plants is crucial for the agriculture sector as plant diseases are widespread. Failure to take appropriate care can lead to serious consequences for plant quality, quantity, or productivity. For instance, a disease called little leaf disease has been found in pine trees in the United States, which can severely affect plant health. Automated methods for detecting plant diseases are useful as they reduce the need for manual inspection in large crop fields, and they can identify disease symptoms as soon as they appear on plant leaves. This paper presents an algorithm for image segmentation for the automatic identification and classification of plant leaf diseases. It also discusses various disease classification methods that can be used for plant leaf disease detection. Genetic algorithms are used for image segmentation, which is a crucial aspect of disease detection in plant leaves. The paper focuses on identifying the Powdery Mildew disease in organic grape products, which can cause significant damage if not detected in time. The accurate identification and classification of diseases from leaf images are crucial for preventing significant losses in output and agricultural inputs. The proposed process can help improve the accuracy and efficiency of disease detection in plants and lead to better agricultural productivity.

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Two-Stream Holographic Convolutional Network for Speech Emotion Recognition Using Korean Audio

Two-Stream Holographic Convolutional Network for Speech Emot...

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Ubiquitous Computing and Intelligent Information Systems (ICUIS), International Conference on

作者： N. Venkateswaran Balaji. Mudadla R. Rajitha Jasmine J Nalini M Rajasekar T. Jayasudha Department of Master of Business Administration Panimalar Engineering College Chennai Tamil Nadu India Department of Computer Science And Engineering (Artificial Intelligence & Machine Learning) CMR Technical Campus Hyderabad Telangana India Department of Information Technology R.M.K. Engineering College Kavaraipettai Tamil Nadu India Department of Computer Science Vignan's Institute of Engineering for Women Visakhapatnam Andhra Pradesh India Department of Computer Science And Application Saveetha College of Liberal arts and Sciences Saveetha Institute of Medical and Technical Sciences Chennai Tamil Nadu India Department of English KPR Institute of Engineering and Technology Coimbatore Tamil Nadu India

ISBN: (数字)9798331529635

ISBN: (纸本)9798331529642

Speech Emotion Recognition (SER) has become an important area of research in the AI domain, helping systems comprehend people's feelings with speech, in such areas as healthcare, automated customer support, and virtual companionship. Emotion detection from Korean speech raises learning issues and demand an understanding of all high-frequency spectrum and time selective features. Despite that these layers work effectively, typical convolutional networks may lose phase information which is so important for emotion distinction especially in languages with tone and phonetics. For this research, the AI-Hub Speech Emotional Database, a large scale Korean speech dataset, is employed to create a Two-Stream Holographic Convolutional Network (TSHCN) for SER. However, the TSHCN architecture employed a two-path structure in which one branch encoded the spectral feature and the other branch encoded the temporal dynamics in sequence, while the phase information was retained using holodynamic convolutions for better classification of emotions. In order to increase the efficiency of the model, the Kookaburra Optimization Algorithm (KOA) is used on the parameters of the weight network, reducing the number of mistakes and the load of calculations as well as increasing the accuracy of the model. As more evident from the previously discussed results, SER performance is notably enhanced by the use of TSHCN with KOA; specifically, the accurate representation of Korean speakers' speech emotions will help pave the way for the development of SER for the Korean language and other complex languages as well. This study thus takes up the shortcoming in the conventional approaches to the task and proposes an optimal and language-independent SER model. The introduced approach attains higher accuracy as 99%.

关键词： Emotion recognition Accuracy Speech recognition computer architecture Medical services Phonetics Feature extraction Convolutional neural networks Optimization Load modeling

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Record Framework Utilization for the Collection of Wireless Device Static Users

Record Framework Utilization for the Collection of Wireless ...

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Advances in Computing, Communication and Applied Informatics (ACCAI), International Conference on

作者： Maganti Syamala Annaji Kuthe V. Selvakumar Alankrita Joshi K. Baranidharan Joshuva Arockia Dhanraj Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram Andhra Pradesh India Department of Computer Science and Engineering K. D. K. College of Engineering Nagpur Maharashtra India Department of Master of Business Administration Sri Sai Ram Institute of Technology Chennai Tamil Nadu India Department of Electronics & Communication Engineering Graphic Era Deemed to be University Dehradun Uttarakhand Department of Master of Business Administration Sri Sairam Institute of Technology Chennai Tamil Nadu India Department of Mechatronics Engineering Centre for Automation and Robotics (ANRO) Hindustan Institute of Technology and Science Chennai Tamil Nadu India

The cell of today is a typical instrument. It has become a significant "social item," as opposed to only a minor "specialized object," in the regular daily existences of its users. In this article, we look at a few ongoing improvements in the examination of mobile phone data. With the developing openness of enormous, anonymised datasets, this field of concentrate previously seemed decade prior and has since formed into an independent subject. The estimating investigation of record framework utilization for a gathering of mobile gadget users in different spots is introduced in this work. The purpose of this research is to refute the notion that mobile system behaviour can be modelled as being uniform across different environments. The research demonstrates that when participants travel between places, they obtain statistically substantially diverse sets of data. These findings call into question how mobile computing systems are normally evaluated, which often models a user's behaviour as being comparable to her behaviour at a single place over a longer length of time.

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An Analysis of the Impact of DNS-Based Authentication on Wireless Network Security

An Analysis of the Impact of DNS-Based Authentication on Wir...

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Emerging Research in Computational science (ICERCS), International Conference on

作者： Piyush P. Gawali Neeraj Das A Rajesh Anupam Shukla Jagmeet Sohal R. Jayadurga Department of Computer Engineering Vishwakarma Institute of Information Technology Pune India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Department of Computer Science Engineering Faculty of Engineering and Technology JAIN (Deemed-to-be University) Bangalore Karnataka India Department of Computer Science & Engineering Vivekananda Global University Jaipur India Centre of Interdisciplinary Research in Business and Technology Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Department of Master of Business Administration Prince Shri Venkateshwara Padmavathy Engineering College Chennai

This paper investigates the effect of DNS-primarily based authentication on wireless network safety. DNS-primarily based authentication is used to authenticate consumer credentials on Wi-Fi networks, generally primarily based upon the Dynamic Host Configuration Protocol (DHCP). The research specializes in checking out the authentication system via numerous simulations, reading the performance and security of the authentication manner. Effects from such studies monitor that the usage of DNS-based total authentication can improve the safety of a Wi-Fi community, specifically in mitigating man-in-the-center assaults. In addition, analysis shows that DNS-based authentication concurrently improves network overall performance using reducing the fee of authentication, decreasing latency, and increasing the scalability of the authentication process. This research suggests that universal, DNS-based authentication is a precious tool to enhance the security of Wi-Fi networks.

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Resilience test case automation for LTE femtocell networks 5

Resilience test case automation for LTE femtocell networks

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5th International Conference on Intelligent Computing and Control Systems, ICICCS 2021

作者： Sudarsanam, P. Anand, R. Banerjee, Sumanta Hemanth, R. BMS Institute of Technology and Management Bangalore Department of Master of Computer Applications Bengaluru India CMR Institute of Technology Bengaluru Department of Information Science and Engineering Bengaluru India Nokia Solutions and Network SQUAD LEAD Bengaluru India BMS Institute of Technology and Management Bangalore Department of MCA Bengaluru India

ISBN: (纸本)9781665412728

The automated test case generation includes the manual test cases, which are converted to automated test cases through executable test scripts. It is more essential to bring out an effective resilience testing for network gateways. The automation testing plays a major role in performing time constrained and skillful work. Moreover, the network gateways will not take part in onetime investment, even when the technology or environment evolves, the test scripts are continuously patched based on the market demands. Thus, the automation testing is remaining as a challenging task to define and test automatically in a cost-effective mode. The developed device is called as femtocell, which helps to effectively densify the network and deliver a great customer experience in small indoor environments. It is checked to meet the real world scenarios under resilience test cases. It is performed by various manual test cases. Moreover, this project automates the manual test cases. © 2021 IEEE.

关键词： Automation

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Vision-Based Locomotion Control for Humanoid Robots: A Study on Vision-Guided Walking Strategies

Vision-Based Locomotion Control for Humanoid Robots: A Study...

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Computational Intelligence and Sustainable Engineering Solutions (CISES), International Conference on

作者： Saroj Kumar Pandey Lalan Kumar Gaurav Kumar Ankit Kumar Kamred Udham Singh Teekam Singh Department of Computer Engineering & Applications GLA University Mathura UP India Department of Master of Computer Application GL Bajaj Institute of Technology and Management India School of Computing Graphic Era Hill University Dehradun India Department of Computer Science and Engineering Graphic Era Deemed to be University Dehradun

Recently, there has been a lot of research on using vision to direct humanoid robots as they walk. Focusing on the major methodologies, difficulties, and prospects, this review article presents an overview of the current state of study in the vision-guided walking of humanoid robots. In the first part of the study, we introduce vision-guided walking and go through the several methods that have been developed to execute it, such as visual serving, visual odometer, and visual simultaneous localization and mapping. The difficulties of vision-guided walking are then discussed; these include, but are not limited to, the necessity for accurate and strong computer vision algorithms, the robot's need to have a solid awareness of its own body and how it moves, and the need to maintain balance while walking. This paper provides a detailed review of the research on humanoid robots that use vision to direct their gait. Deep learning for vision-guided walking, tactile and proprioceptive sensing combined with vision, and reinforcement learning for adaptive walking are only a few of the issues covered in this overview. Future directions in vision-guided walking research are discussed in this paper. These include the development of more advanced and reliable computer vision algorithms, the incorporation of other modalities like tactile and auditory sensing, and the investigation of new applications for vision-guided walking in fields like healthcare and rehabilitation.

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Predictive Design for Quality Assessment Employing Cloud Computing And Machine Learning

Predictive Design for Quality Assessment Employing Cloud Com...

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Advance Computing and Innovative Technologies in Engineering (ICACITE), International Conference on

作者： Gaurav Jindal Vidhika Tiwari Riyaz Mahomad Anita Gehlot Mimoh Jindal Dibyhash Bordoloi Department of Master of Computer Applications G L Bajaj College of Technology and Management Gr. Noida Mechanical Engineering Department DPGITM Gurugram School of Computer Science and Artificial Intelligence SR University Warangal Telangana India Uttaranchal Institute of Technology Uttaranchal University India National Informatics Centre Delhi India Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun Uttarakhand India

In this work, we investigate a unique effective framework for projected design inspections in industrial manufacturing combining machine learning methodologies and edges cloud computing technologies. We recommend a thorough approach that involves targeted data gathering and broadcast, forecasting and supplying appropriate, as well as innovation with the current IT plant infrastructure, in contrary to state-of-the-art contributions. To highlight the steps and advantages of the suggested solution, a genuine business use case in the manufacture of SMT is described. The outcomes demonstrate that the proposed strategy can dramatically reduce inspection volumes, leading to economic gains. A key achievement basis for the drawn-out presentation of assembling ventures is the creation of imperfection-free, top-notch items. Indeed, even with the intricacy and assortment of items and the requirement for savvy production, an exhaustive and dependable quality investigation is habitually required. Along these lines, high examination volumes cause producing bottlenecks in the review processes. Thus, the purpose of this paper is to develop a predictive design for quality assessment regarding employing cloud computing and machine learning. Concerning this particular research, a descriptive research design has been used by depending on secondary data due to its reliability and validity. Moreover, it has been found that both cloud computing and machine learning contribute effectively for employing quality assessment because of their innovative features and infrastructure.

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A Study and Analysis on Various Types of Agricultural Drones and its Applications 5

A Study and Analysis on Various Types of Agricultural Drones...

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5th International Conference on Research in Computational Intelligence and Communication Networks, ICRCICN 2020

作者： Dileep, M.R. Navaneeth, A.V. Ullagaddi, Savita Danti, Ajit Nitte Meenakshi Institute of Technology Department of Master of Computer Applications Bengaluru India Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9781728188188

Drones are considered to be the greatest invention of mankind. Drones can be used in many areas widely. Drones can also be used in agriculture and it is called as unnamed aerial vehicles (UAV). In the traditional agriculture methods land vehicles are used to monitor various activities of the agriculture, this was consuming lot of human effort and time. Using drones in agriculture is more beneficial than using traditional methods for the activities. Usage of drones in agriculture provides a huge benefit in terms of economy and time due to their most astonishing features. In recent years many surveys have proved that drones can cover almost 10 to 15 times of the area which can be covered with traditional land based techniques. Drones can be controlled by computers according to their capacities, that is drones can be automated over some range of area, locating remote area, and even can be semi-automated. Drones can be efficiently used in agriculture for performing certain activities such as, studying weather conditions and variations, infection for the crops, land fertility and many more. Because of the efficiency of the drones they can be used in various activities of agriculture. In this paper, a detailed study has been made on various types of agricultural drones based on the feature, capacity, range as well as cost and the area of agriculture where they suit the most, and a statistical analysis about the usage of the drones in the field of agriculture. © 2020 IEEE.

关键词： Drones

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