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A Real-Time Video Surveillance-Based Framework for Early Plant Disease Detection Using Jetson TX1 and Novel LeafNet-104 Algorithm 4th

A Real-Time Video Surveillance-Based Framework for Early Pla...

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4th international conference on Frontiers in computing and Systems, COMSYS 2023

作者： Murthy, Jamuna S. Dhanashekar, K. Siddesh, G.M. Department of Computer Science and Engineering Ramaiah Institute of Technology Bengaluru India Department of Artificial Intelligence and Data Science Ramaiah Institute of Technology Bengaluru India The Ohio State University Columbus United States

ISBN: (纸本)9789819726134

The precise and timely identification of plant diseases plays a pivotal role in safeguarding crop health and optimizing agricultural yields. This paper introduces an innovative framework designed for plant disease detection using video surveillance-based cameras strategically deployed on the Nvidia Jetson TX1 hardware platform within agricultural farms. Leveraging the capabilities of deep learning and the novel neural network architecture known as LeafNet-104, this framework empowers real-time disease detection on plants. Through the deployment of video surveillance cameras, continuous monitoring and early disease detection become achievable, enabling farmers to take prompt corrective measures and mitigate further crop damage. The Nvidia Jetson TX1, with its robust high-performance computing capabilities, enables on-device processing, rendering it ideal for real-time applications. To assess the efficacy of the proposed framework, comprehensive experiments were conducted on an actual agricultural farm. The dataset encompassed various plant species and disease types. Evaluation metrics such as accuracy, precision, recall, and the F1-score were employed to gauge the performance of the LeafNet-104 algorithm. The experimental findings underscored the framework's capacity to accurately and swiftly detect and classify plant diseases in real time, attaining an impressive accuracy rate of 97.5%. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Crops

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Consumer Buying Behavior Analysis During COVID-19 Phase Using Statistical data Mining and Multi-verse Stochastic Algorithm 5th

Consumer Buying Behavior Analysis During COVID-19 Phase Usin...

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5th international conference on Recent Innovations in computing , ICRIC 2022

作者： Sinha, Anurag Bhargavi, Mopuru Singh, N.K. Narayan, Devendra Garg, Namit Pal, Siddhartha Department of Computer Science IGNOU New Delhi India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Guntur Vaddeswaram India Department of Computer Science BIT Mesra Jharkhand Ranchi India Department of Biotechnology Amity University Jharkhand Ranchi India Department of Mathematics and Statistics IIT Kanpur Uttar Pradesh Kanpur India Department of Physics IIT Kanpur Uttar Pradesh Kanpur India

ISBN: (纸本)9789819906000

COVID-19 has changed particularly the marketing and retail sectors, the epidemic has changed peoples’ habits. Analyzing client purchase patterns and flows is a common use for consumer behavior analysis. For the purpose of analyzing consumer behavior and purchase trends across the COVID-19 era, this work combined a statistical strategy with a data mining approach. Furthermore, a survey-based (online) data analysis was conducted for the evaluation by retailers and customers, filling out a questionnaire that comprised demographic information and product associations obtained during the epidemic. In order to test the association rule of data mining, the information for this study was gathered from a nearby grocery. Additionally, main data was converted to secondary data using the meta-verse algorithm (MOA) (balanced). One of the newest meta-heuristic optimization algorithms, multi-verse optimization (MVO), imitates the multiversity hypothesis of physics and simulates the interaction of many universes. This MOA is based on natural phenomena that employed a stochastic method to accomplish its objective. Finally, statistical analysis has been carried out to look into the purchasing and selling trends of retailers within the dataset. In this method, association rules are generated via pincher search. It counts the supports of the candidate in each run using a bottom-up approach in addition to the supports of chosen item sets using a top-down approach. The Maximum Frequent Candidate Set refers to these. The proposed binary versions transfer a continuous version of the MVO algorithm to its binary counterparts using the idea of transformation functions. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Association rules

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An Approach to the Presumptive Detection of Road Incidents in Cuenca, Ecuador Using the data from the Social Media Twitter and Spanish Natural Language processing 3rd

An Approach to the Presumptive Detection of Road Incident...

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3rd international conference on Smart Technologies, Systems and Applications, SmartTech-IC 2022

作者： Loja-Morocho, Pablo E. Reyes-Duchitanga, Robbyn T. León-Paredes, Gabriel A. Grupo de Investigación en Cloud Computing Smart Cities & High Performance Computing Universidad Politécnica Salesiana Cuenca Ecuador

ISBN: (纸本)9783031322129

The road situation in Ecuador, specifically in the city of Cuenca and its surroundings, can vary according to various aspects related mainly to the weather, or traffic incidents caused due to reckless actions by drivers. Therefore, many times these problems can cause some drivers to be forced to delay their trip or look for alternative routes to reach their destination, others can plan their departure according to the related news they find when browsing social media, which can be somewhat time-consuming and even unlikely to find. For this reason, in this paper, a mobile application is presented whose primary function is to collect publications from social media Twitter from different Twitter accounts that are related to road incidents and to classify them using Natural Language processing (NLP) and Machine Learning (ML) models such as BERT. Then, we present an approximate location of the presumptive incident on an interactive map which allows each of these tweets to be displayed with a personalized icon that can indicate different types of road incidents and, at the same time, the details of the event that occurred. Two main experiments were carried out to find (a) the model accuracy, where a value of 80% was obtained as a result, which is considered a positive result for road incident classification, and (b) the execution of acceptance tests, where a questionnaire based on the ISO 9126 standard was presented to a group of bikers belonging to the city of Cuenca, obtaining as a result that for most users the application is new (85.8%), efficient (85.7%) and easy to use (64.3%). This research makes it possible to present news of road incidents in a centralized manner, which makes it easier for drivers to stay informed and thus avoid annoying interruptions in their circulation. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Machine learning

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Forecasting Stock Prices using Gated Recurrent Unit with the Help of Feature Engineering

Forecasting Stock Prices using Gated Recurrent Unit with the...

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Sustainable computing and Smart Systems (ICSCSS), international conference on

作者： Challa Vijay Seshachala Sarma Bezawada Sri Bharath Krishna Buyyanapragada Phani Siva Baradwaz Yedupati Sai Madhav Sunitha Pachala Vuyyuru Lakshmi Lalitha Artificial Intelligence and Data Science KL University Vijayawada India AI&DS department Koneru Lakshmaiah Education Foundation Vaddeswaram AP India

Forecasting the stock prices is one of the hardest tasks because of its volatility and uncertainty, where additional features such as foreign exchange impacts the stock markets. Introducing AI in forecasting has helped in prediction tasks, especially the time series algorithms. This study attempts to implement various regression models such as the ARIMA model, which gives us improved results for data of linear nature but it suffers from conditions such as non-linearity, unable to cope up with dynamic changes in prices, and overdependence on historical data. This study attempts to implement ML and DL mechanisms to predict stock prices. As most stock markets deal with probabilistic functions and mathematical computations, usage of technologies such include algorithms for machine learning and deep learning. The proposed objective is to forecast the stock prices to enable the users to make decisions and undertake profitable trades within certain intervals by using GRU (Gated Recurrent Unit).

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The Effect of Wooden Floor processing Procedures on Product Quality 15th

The Effect of Wooden Floor Processing Procedures on Product ...

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15th international conference on Genetic and Evolutionary computing, ICGEC 2023

作者： Wang, Tien-Chin Hung, Tzu-Lien Yu, Jiunn-Ming Department of International Business National Kaohsiung University of Science and Technology No. 415 Jiangong Road Sanmin District Kaohsiung City807618 Taiwan

ISBN: (纸本)9789819994113

The study uses t-test, ANOVA, and regression to test differences between products from author’s company, including 10 wooden floors, 3 super wear-resistant floors as well as 5 different types of coating, all of which are currently well-received and frequently selected by customers. Despite being commonly selected by customers, they are also the hardest to present in dialectic. Therefore, the aforementioned various materials are selected as study materials, with L Company’s materials for experimentation, intended for three different types of experiments. Five types of uncoated wooden flooring are tested for wear-resistance, three different materials tested for wear-resistance, hardness of five different types of coating are tested. L Company’s products reveal, that empirical analysis witnessed the wear-resistance, UV proved most resistant of all five coatings, whereas PU proved least wear-resistant. Though with prolonged use, different coatings will present certain levels of wear. For analysis of five wood types, rosewood proved more wear-resistant, followed by teak, with oak being least wear-resistant. Though each type of wood differs in initial density, they are structurally identical and will not soften with increased wear, but instead reduce in size uniformly. Conclusion, data acquired from the tests further substantiate empirical consistency. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Hardness

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The 5G Edge computing using Deep Reinforcement Learning for data processing

The 5G Edge Computing using Deep Reinforcement Learning for ...

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Smart Generation computing, Communication and Networking (SMART GENCON), international conference on

作者： Manish Nagpal Centre for Interdisciplinary Research in Business and Technology Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

Due to the absence of methods and dedicated equipment for underground 5G applications, data processing latency is significant in coal mines and transmission efficiency is low. To combat this problem, scientists are investigating how to improve underground 5G communication through edge caching and automatically allocating resources based on the actual situation in coal mines, using a technology called mobile edge processing (MEC), which is built on 5G wireless base stations. Based on experimental data, it can be concluded that the median delay of all local execution techniques is 30 ms, whereas the mean delay of completed jobs is 15 ms, provided no modifications are made to the present base station's reported power or transmission power. Compared to other MEC execution techniques, this one has the lowest average time, by 37.5%. A single base stations has the potential to achieve throughputs of 1 Gbps on the uplink and 1.5 Gbps on the downlink simultaneously. Our approach has the potential to considerably enhance the efficiency with which resources are allocated in mining 5G communication networks.

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A study of base station establishment site selection based on cluster analysis

A study of base station establishment site selection based o...

引用

Electrical Engineering, Big data and algorithms (EEBDA), IEEE international conference on

作者： Runxi Luo School of Electronic Information Engineering Wuhan Donghu University Wuhan China

In this paper, to address the site planning and area clustering problems of mobile communication networks, the K-mean clustering algorithm, linear programming, K-mean clustering model, single-objective planning model, and simulation model are established, and python is used to program the solution to obtain the optimal site coordinates of new base stations and base station types, the optimal main direction angle of base stations and the total business volume of weak coverage points covered by base stations, how to cut and divide the area so that The paper features a series of conclusions such as how to cut and divide the area to minimize the total time complexity of clustering. This paper features the analytical processing of data and the establishment and application of K-mean clustering model.

关键词： Base stations Solid modeling Analytical models Clustering algorithms Programming Mobile communication data models

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international conference on Modern Electronics Devices and Communication Systems, MEDCOM 2021

International Conference on Modern Electronics Devices and C...

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international conference on Modern Electronics Devices and Communication Systems, MEDCOM 2021

ISBN: (纸本)9789811963827

The proceedings contain 46 papers. The special focus in this conference is on Modern Electronics Devices and Communication Systems. The topics include: Medical Image Analysis Using Soft computing Feature Selection and Classification of Skin Cancer;Comparative Study of Fuzzy, PID, and 2DOF-PID Controller for Liquid Level Control System;IOT-Based Self-nourishing System for Plants;Social Distance Alerting Device for COVID-19;BWDS-Based Spatially Dabble Couple Horizontal-Vertical 4-Port PIPO DRA with Poly-Pattern Diversity;Design of U-Shaped MIMO Antenna for Wi-Fi Application;comparative Study of Parametric Disturbances Effect on Cart-Inverted Pendulum System Stabilization;Efficient Design of QCA ADD-SUB Subsystem for Future Microprocessors;predication of Diabetes Through Machine Learning Technique;comparative Analysis for Email Spam Detection Using Machine Learning algorithms;Study of Gate Workfunction Modulated FinFET with Effect of TID;prediction of Accuracy and Performance of Crop Production by Using Machine Learning Algorithm;cognitive Load Classification in Alcoholic and Control Subjects Performing Delayed Matching Task Using Optimizable Classifier Networks;energy Detection in Cognitive Radio Networks to Optimize the Spectrum Utilization Through Cooperative Spectrum Sensing Using Weights;Real-Time Tracking of Health Parameters Using IOT and data Logger: Application to COVID-19;Real-Time Facial Detection Using MATLAB (Utilizing VJ Method);Hardening of EMP in Shielded Enclosure for Radiated Fields;Review on Optimizing Text-Video Retrieval Using CLIP (Contrastive Language-Image Pre-training);argument Mining: A Categorical Review;optimizing Text: Representation of Similarity Using Machine Learning;post-quantum Cryptography: A Solution to the Challenges of Classical Encryption algorithms;experimental Analysis on Aging Evaluation of Transformer Oil via data Collection;designing Adiabatic Techniques for Logic Circuits.

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Analysis Techniques for Pharmaceutical Drugs Biomedicine Big data Analytics and Machine Learning

Analysis Techniques for Pharmaceutical Drugs Biomedicine Big...

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international conference on ent computing and Control Systems (ICICCS)

作者： P A Padmaavathy A. Celina R. Prameela Devi B N Arathi G. Sureshkumar G. Ramachandran Department of Management Faculty of Arts Science commerce and Management Karpagam Academy of Higher Education Coimbatore College of Management SRMIST Kattankulathur Department of ECE CVR College of Engineering Hyderabad Telangana Dept of Computer science and Engineering PES University Bengaluru Karnataka ECE Vinayaka Mission’s Kirupananda Variyar Engineering College Vinayaka Mission’s Research Foundation (Deemed to be University) Salem

The phases of drug discovery and development, starting with drug design and moving through crucial clinical trials to clinical practice, are being revolutionized by machine learning techniques. A more sophisticated kind of learning is machine learning (ML), which may be used to identify patterns in the solutions to solve current problems and learn to solve unsolved problems in future. Following the pre-processing of the necessary data, the objective is to learn from the data and identify patterns. The results can then be obtained by applying the patterns found to a different dataset. In general, when using larger, more agile data samples on high-dimensional datasets, ML algorithms can make predictions that are more accurate. Along with the rapid development of bioinformatics, there has been a significant global evolution in drug discovery and design technologies over the past ten years. Various artificial intelligence and machine learning (ML) methodologies have been used in drug discovery and design, leading to changes in all of their stages, including the time-consuming drug-target interaction (DTI) prediction and medicines and compounds discrimination.

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An Analysis of Cancer data Sets Utilizing data Mining

An Analysis of Cancer Data Sets Utilizing Data Mining

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Advances in computing, Communication and Applied Informatics (ACCAI), international conference on

作者： Bhaludra R Nadh Singh Sripuram Sai Keerthi Vantari Sai Nikitha Sama Sai Sradha Neelagiri Shiva Rithika Shivani Dornala Department of Computer Science and Engineering Bhoj Reddy Engineering College for Women Hyderabad-59 Telangana India Bhoj Reddy Engineering College for Women Hyderabad-59 Telangana India

This research aims to ensure precision in medical outcomes by comparing and contrasting classification approaches using various (Lucamia) cancer knowledge sets using data processing technologies. Many researchers have investigated this question, and their findings have lent credence to using specialised knowledge bases and classifiers. They have compared and contrasted various knowledge sets, including those found online. Here, we compare the results of a neural network classification with those obtained without one.

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