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Improved top-down attention phasmatodea population spiking neural network based multi crop recommendation framework

Multimedia Tools and Applications 2025年第10期84卷 8071-8105页

作者： R, Chetan Ashoka, D.V. B, Ajay Prakash Department of Computer Science and Engineering JSS Academy of Technical Education Visvesvaraya Technological University Karnataka India Department of Information Science JSS Academy of Technical Education Visvesvaraya Technological University Karnataka India Department of Information Science and Engineering JSS Academy of Technical Education Visvesvaraya Technological University Karnataka India Department of Artificial Intelligence and Machine Learning Dr. Ambedkar Institute of Technology Visvesvaraya Technological University Karnataka India

Modern agriculture refers to the contemporary practices, technologies and methods used in food production to meet the growing global demand for agricultural products. With increasing concerns about environmental degradation, soil health and resource depletion, there is an insistent need for agricultural practices that are both productive and sustainable. Multicropping mitigate the negative impacts of monoculture and reduce the reliance on synthetic inputs. This research introduces an innovative approach for multi-crop recommendation through the utilization of an integrated framework that combines advanced optimization techniques and neural networks like Enhanced top-down attention Spiking Neural Network (ETSNN). The proposed framework involves two main phases: the first phase focuses on classifying soils suitable for specific agro-ecological zones using an enhanced top-down attention spiking neural network. The second phase predicts multi-crop yields across different districts based on various environmental parameters using an Improved Phasmatodea population Evolutionary Algorithm (IPEA). Before classifying the crops feature selection takes place for the dataset by Enhanced Sea horse Optimization (ESO) method. The outputs from both phases are harmonized using a stacking-based fusion method. The developed user interface accepts real-time input for location-specific soil properties and provides crop recommendations that take into account both the input data and climate parameters. The proposed method attains 99.7% accuracy, 99.2% precision, 99% recall and 99.9% F1-score respectively. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Crops

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Design Principles and a Software Reference Architecture for Big Data Question Answering Systems 25

Design Principles and a Software Reference Architecture for ...

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25th International Conference on Enterprise Information Systems, ICEIS 2023

作者： Moraes, Leonardo Mauro Pereira Jardim, Pedro Calciolari Aguiar, Cristina Dutra Department of Computer Science University of São Paulo São Carlos Brazil Machine Learning & Artificial Intelligence Sinch Stockholm Sweden

ISBN: (纸本)9789897586484

Companies continuously produce several documents containing valuable information for users. However, querying these documents is challenging, mainly because of the heterogeneity and volume of documents available. In this work, we investigate the challenge of developing a Big Data Question Answering system, i.e., a system that provides a unified, reliable, and accurate way to query documents through naturally asked questions. We define a set of design principles and introduce BigQA, the first software reference architecture to meet these design principles. The architecture consists of high-level layers and is independent of programming language, technology, querying and answering algorithms. BigQA was validated through a pharmaceutical case study managing over 18k documents from Wikipedia articles and FAQ about Coronavirus. The results demonstrated the applicability of BigQA to real-world applications. In addition, we conducted 27 experiments on three open-domain datasets and compared the recall results of the well-established BM25, TF-IDF, and Dense Passage Retriever algorithms to find the most appropriate generic querying algorithm. According to the experiments, BM25 provided the highest overall performance. Copyright © 2023 by SCITEPRESS - science and Technology Publications, Lda. Under CC license (CC BY-NC-ND 4.0)

关键词： Big data

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Precision Land Use and Land Cover Mapping with a High-Accuracy Time Series LSTM Model

Precision Land Use and Land Cover Mapping with a High-Accura...

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2025 IEEE International Students' Conference on Electrical, Electronics and computer science, SCEECS 2025

作者： Aryan, Adla Rv, Prakash Aluvala, Srinivas Chowdary, Venigalla Shashank Ardhaman College of Engineering Department of Artificial Intelligence & Machine Learning Telangana Hyderabad501218 India Sr University Department of Computer Science & Artificial Intelligence Telangana Warangal506371 India Nandyal518501 India

ISBN: (纸本)9798331529833

This paper introduces a machine-learning-based LULC classifying model developed with a time series data set, trained on a diverse set of agriculture and non-agricultural classes. It used a strong generalization capability employing an LSTM architecture;that is, it achieved accuracy of 90.04% in training and also validation accuracy of 90.55%. The ultimate accuracy was obtained at the minimal loss of 0.1909 at the accuracy of 95.58%, thereby validating its usability for LULC pattern classification. This model has a wide-range of potential application in environmental monitoring and resource management. Future work involves introducing more variables into the dataset, the inclusion of other environmental factors, and fine-tuning of the model towards more general applications in geospatial applications. Thus, this work advances the current state of remote sensing and LULC classification to present an effective tool in sustainable land management. © 2025 IEEE.

关键词： Environmental monitoring

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On the Equivalence of Maximum Reaction Time and Maximum Data Age for Cause-Effect Chains 35

On the Equivalence of Maximum Reaction Time and Maximum Data...

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35th Euromicro Conference on Real-Time Systems, ECRTS 2023

作者： Günzel, Mario Teper, Harun Chen, Kuan-Hsun von der Brüggen, Georg Chen, Jian-Jia Department of Computer Science TU Dortmund University Germany University of Twente Netherlands Lamarr Institute for Machine Learning and Artificial Intelligence Dortmund Germany

ISBN: (纸本)9783959772808

Real-time systems require a formal guarantee of timing-constraints, not only for individual tasks but also for data-propagation. The timing behavior of data-propagation paths in a given system is typically described by its maximum reaction time and its maximum data age. This paper shows that they are equivalent. To reach this conclusion, partitioned job chains are introduced, which consist of one immediate forward and one immediate backward job chain. Such partitioned job chains are proven to describe maximum reaction time and maximum data age in a universal manner. This universal description does not only show the equivalence of maximum reaction time and maximum data age, but can also be exploited to speed up the computation of such significantly. In particular, the speed-up for synthesized task sets based on automotive benchmarks can be up to 1600. Since only very few non-restrictive assumptions are made, the equivalence of maximum data age and maximum reaction time holds for almost any scheduling mechanism and even for tasks which do not adhere to the typical periodic or sporadic task model. This observation is supported by a simulation of a ROS2 navigation system. © Mario Günzel, Harun Teper, Kuan-Hsun Chen, Georg von der Brüggen, and Jian-Jia Chen.

关键词： Real time systems

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Comparison of Training Function, Adaption learning Function, and Transfer Function of Hidden Layers in Artificial Neural Network in Weather Prediction 9

Comparison of Training Function, Adaption Learning Function,...

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9th International Conference on Information Management and Technology, ICIMTech 2024

作者： Dewi, Adhe Lingga Setyoko, Dimas Elang Setyo Adhi, Canggih Gelar Afrilia, Nur Sitha School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia Bina Nusantara University Digital Language Learning Center Faculty of Humanity Jakarta11480 Indonesia

ISBN: (纸本)9798350390025

Weather prediction plays an important role in human life, such as agricultural commodities, aquaculture, trade, industry, tourism systems, airport/flight systems, mining industry, etc. This study is to compare the performance of several Training Function, Adaption learning Function and Transfer Function in Artificial Neural Networks for weather prediction. The data used is historical weather that occurred in Semarang City, Indonesia from January 1, 2012, to December 31, 2022, as training data. Meanwhile, the data used as test data is historical weather data that occurred during the period from January 1, 2023, to December 31, 2023. The weather data consists of average temperature, humidity, and wind speed. Based on the training results, using trainlm, learngdm, and tansig which was trained using 30 hidden layers and 1000 epochs has the lowest MSE value of 5.08. Apart from that, the regression value of the training data also showed good results. That is, 0.99091 for details, 0.99102 for training, 0.98922 for validation, and 0.99263 for testing. The ANN architecture that has been trained using the configuration mentioned is used to make predictions on test data. The test results show that the ANN prediction error for temperature is 1.45% and humidity is 6.68%. Based on the results, it is proven that comparing three key factors of ANN for weather forecasting has a significant impact on the weather prediction ability of Artificial Neural Network. © 2024 IEEE.

关键词： Weather forecasting

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African Vultures Based Feature Selection with Multi-modal Deep learning for Automatic Seizure Prediction 2

African Vultures Based Feature Selection with Multi-modal De...

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2nd International Conference on Distributed Computing and Optimization Techniques, ICDCOT 2024

作者： Nallur, Mounika Sandhya, M. Khan, Zabiha Mohan, B.R. Nayana, C.P. Rajashekhar, S.A. Murray State University Computer Information Systems United States East West Institute of Technology Department of Computer Science and Engineering Bengaluru India Nitte Meenakshi Institute of Technology Department of Artificial Intelligence & Machine Learning Bengaluru India

ISBN: (纸本)9798350382952

The superiority of life for people with epilepsy can be greatly improved with the assistance of accurate seizure prediction and early warning. An automatic prediction model is required to procedure the EEG signals and account for the leads optimization problematic, as opposed to the majority of hand-designed prediction approaches. In this research, we put forth a fully automated model for seizure prediction using Channel and Spatial attention (CASA). The first step in the feature extraction practice is to pre-process the raw EEG signals. Large amounts of computation can be saved by adding more features to the system, but finding the right ones can be tricky. The African vulture optimization algorithm's (AVOA) strong capacity to break out of local optima is what makes this procedure possible. CASA saved the raw EEG data's temporal and geographical details. Automatic optimization of EEG full-lead data was completed with channel attention (CA), leading to an increase in the accuracy of predictions. The aforementioned adaptive learning of feature parameters was accomplished via spatial attention (SA). When all else fails, a fully associated layer is used to make the seizure forecast. The suggested algorithm is tested on the Freiburg EEG database, and the results reveal that the AVOA-based system performs admirably when it comes to predicting seizures. © 2024 IEEE.

关键词： Electroencephalography

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Passive RFID Encryption and Decryption using CLI 7

Passive RFID Encryption and Decryption using CLI

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7th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2023

作者： Sailesh Kumar, K.L. Srivatsan, M. Suhas Kumar, S.J. Satyaa, Sudarshan Vishwamalyan, J.S. Vellore Institute of Technology Department of Computer Science and Engineering Vellore India Rajalakshmi Engineering College Department of Computer Science and Engineering Chennai India Rajalakshmi Engineering College Department of Artificial Intelligence and Machine Learning Chennai India

ISBN: (纸本)9798350341485

The implementation of the concept of RFID encryption to make a system that prevents the attack on the data of any user or organization on the cloud. The built application has a command line interface. The command line interface is used here instead of a web server to prevent data leakage during data transmission and reduce the risk of a rogue web server. The command line interface takes in an input image and encrypts it, producing an encrypted image and a key file. The CLI also decrypts the previously encrypted image using the key file. This app as a whole solves the problem of users and organizations that store images on the cloud, using this app the user can store encrypted images on the web while the low storage key file is on the user/organization's on-site server. This provides a 2-tier security mechanism via which an adversary will need to hack both the organization server and the cloud server to compromise the data, this way the image data is secure without any additional cost or overhead to the organization. an RFID encryption algorithm is developed along with a non-network-based CLI that secures the organization's RFID data and helps implement a secure cloud-bursting architecture. In this project, an algorithm is developed by combining both the XOR-based encryption algorithm and non-network-based CLI to secure the RFID of the user/organization. Most encryption algorithms nowadays use web-based servers to encrypt the RFID and produce the key file. This makes web-based encryption insecure and vulnerable to a man-in-the-middle attack. Therefore, this app comes to the rescue as it provides a non-network-based CLI by which there is no need for the actual RFID to go outside of the local machine, thus making this app and its architecture secure and robust. © 2023 IEEE.

关键词： Radio frequency identification (RFID)

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Document Data Extraction with Optical Character Recognition 14

Document Data Extraction with Optical Character Recognition

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14th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2023

作者： Sanghvi, Vidit Ukani, Vijay Patel, Raj Sharma, Priyanka Shah, Pooja Computer Science and Engineering Department Institute of Technology Nirma University Gujarat Ahmedabad India Department of Machine Learning Samyak Infotech Private Limited Ahmedabad India

The modern solutions for fetching content from PDF documents or images includes fetching document’s multiple features like processing using Natural Language Processing (NLP), border-less table contents, processing it’s stacked lines, fetching document layout information and working on region-based features from documents. There are new challenges like fetching a table’s content where the table is included in borders of other content. In this paper, we have used LayoutLM for parsing the content and RetinaNet for table detection in a particular document. The pretrained models were trained on very less data-set to decide for large scale use. We also have discussed architecture of LayoutLM and tesseract that we used in building the tool with discussing output of some other approaches. © Grenze Scientific Society, 2023.

关键词： Optical character recognition

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A Multi-phase Multi-graph Approach for Focal Liver Lesion Classification on CT Scans 17th

A Multi-phase Multi-graph Approach for Focal Liver Lesion C...

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17th Asian Conference on computer Vision, ACCV 2024

作者： Sam, Tran Bao Huy, Ta Duc Dao, Cong Tuyen Lam, Thanh Tin Tang, Van Ha Truong, Steven Q. H. Vinbrain JSC Hanoi Viet Nam Australian Institute for Machine Learning University of Adelaide Adelaide Australia Department of Computer Science Le Quy Don Technical University Hanoi Viet Nam

ISBN: (纸本)9789819609710

Liver cancer remains a leading cause of global mortality, driving interest in computer-aided diagnosis for liver tumor detection. Existing methods typically focus on individual lesions and avoid the impact of neighboring tumors on diagnostic accuracy. This study introduces a novel multi-phase multi-graph (MPMG) approach to improve liver tumor classification using contrast-enhanced computed tomography (CECT) scans. The MPMG method models inter-lesion relationships, including the ratio of diameters, semantic similarity, physical distance, and neighbor influence score as graph edge embeddings, while multiphasic features extracted from a proposed deep convolutional neural network form the node representations. By analysing different edge embedding formations, we find through extensive experiments that the proposed MPMG model outperforms several state-of-the-art methods in liver tumor diagnosis. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Convolutional neural networks

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Predicting Soil Types and Crop Suitability through Comprehensive Soil Analysis

Predicting Soil Types and Crop Suitability through Comprehen...

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2024 International Conference on Communication, Computing and Internet of Things, IC3IoT 2024

作者： Sivakumar, S. Dharani, U. Hephzibah, Dineep Srm Institute Of Science And Technology Department Of Computer Science And Engineering Tiruchirapalli India Srm Institute Of Science And Technology Dept. Of Comp. Sci. And Engineering Specialization In Artificial Intelligence And Machine Learning Tiruchirapalli India

ISBN: (纸本)9798350352689

The analysis of soil nutrients and water levels holds utmost significance in the realms of agriculture. Determining the soil type and identifying the crop suitable for that specific soil are critical factors in optimizing agricultural yield. These considerations play a pivotal role in precision agriculture, ensuring that the chosen crops align with the soil's characteristics. This strategic alignment enhances the efficiency of cultivation practices, promotes sustainable agricultural methods, and ultimately contributes to increased crop yields. In this research paper, we systematically identify key soil properties, including NPK concentrations, temperature, humidity, and soil pH. The collected dataset, encompassing these crucial soil attributes, is utilized to train machine learning models. The primary goal is to forecast the soil type utilizing the gathered data, and subsequently, undertake further analyses, to ascertain the relevant crop types that are most suitable for the identified soil characteristics. This comprehensive approach involves the integration of data-driven insights and machine learning techniques to enhance our understanding of soil composition and facilitate informed decision-making in agriculture. © 2024 IEEE.

关键词： Soil testing

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