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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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Enhanced AI Based Diabetic Risk Prediction Using Feature Scaled Ensemble learning Technique Based on Cloud Computing

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SN computer science 2024年第8期5卷 1-11页

作者： Manivannan, K. Ramkumar, K. Krishnamurthy, Revatthy Department of Information Technology V.S.B. Engineering College Tamilnadu Karur India Department of Computer Science and Engineering Rajalakshmi Institute of Technology Tamilnadu Chennai India Department of Artificial Intelligence & amp Machine Learning Rajalakshmi Engineering College Tamilnadu Chennai India

Diabetes causes many health problems including microvascular disease, macrovascular abnormalities and neuropathy. Diabetes is one of the costliest diseases from an economic perspective, and most adults through diabetes live in small- and intermediate-revenue countries, creating additional financial burdens and problems for these *** healthcare industry is one ofvast industries in the comprehensivebudget with revenue and investment increasing every year. Early and exactanalysis of diabetes, especially in the initial stages of the disease, can be difficult for medical professionals. To resolve the issue, we deploy the AI based ensemble learning method. To attain the objective, we perform three stagesfor instance preprocessing, feature selection, and classification. In the first phase, we preprocess the diabetes dataset by using numeric normalization method. This method is used for the diabetes dataset improves the validity of subsequent ML progression and produces more reliable and descriptive results. After preprocessed the dataset we go to select the features based on Correlation-based feature selection (CFS) method. CFS aims to select a feature subset with strong predictive power while minimizing redundancy by estimating the correlation between features and target variables and cross-correlation between features. The proposed method needs to select the important features for classification. At last, we classify the diabetes dataset on the basis ofreviewed ensemble learning methodologies to progress prediction accurateness. A comparison of the review methods for diabetes risk prediction includes K-Nearest Neighbor (K-NN), Whale Optimization and Fuzzy Neural Network (WO-FNN), Decision Tree (DT), and Support Vector machine (SVM). In this paper, we use K-NN, WO-FNN and DT as the base models and SVM as the meta-model. In the ensemble learning methodology we examine that SVM is the best method for forecasting the diabetes at premature stages by the diabetes dat

关键词： Accurate diagnosis CFS Clinical data Diabetes Healthcare industry Numeric normalization method SVM

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Impacts of Fake Reviews on Dietary Supplements and Healthcare Products in Social Media 15

Impacts of Fake Reviews on Dietary Supplements and Healthcar...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Kannal, Nithin M Asmathunnisa, N. Kallimani, Jagadish S M S Ramaiah Institute of Technology Department of Artificial Intelligence and Machine Learning Karnataka Bangalore India M S Ramaiah Institute of Technology Department of Computer Science and Engineering Bangalore India Visvesvaraya Technological University Karnataka Belagavi India

ISBN: (纸本)9798350370249

In today's digital landscape, the prevalence of misinformation is a pressing concern, particularly on social media platforms where approximately $42.8 \%$ of shared content is identified as inaccurate and fake, as highlighted by a 2019 study from Loughborough University's Online Civic Culture Centre. This phenomenon extends beyond mere inconvenience, with false information on political issues and product reviews posing significant threats to societal trust, business integrity, and political discourse. Furthermore, 'The State of Fake Reviews - Statistics and Trends' by InvespCRO (2023) underscores a concerning trend: the steady rise in fake reviews from 2019 to 2023, indicative of a persistent challenge in maintaining online authenticity. This article delves into the intriguing world of online reviews, focusing specifically on the impact of falsified reviews within the realm of supplements and health products. With the power to sway consumer opinions and shape company reputations, false reviews wield considerable influence, often distorting market perceptions and consumer choices. By immersing ourselves in a hypothetical scenario where a product garners an alluring yet deceitful review, we unravel the ripple effects across various sectors, illuminating the far-reaching consequences of deceptive practices. © 2024 IEEE.

关键词： Dietary Supplements Fake Reviews Global Forecast on Online Products Healthcare Products Social Media

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Building a Data Lake for Power BI in the Cloud: A Review on Utilizing Cloud Storage Services for Large Datasets 2

Building a Data Lake for Power BI in the Cloud: A Review on ...

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2nd DMIHER International Conference on artificial intelligence in Healthcare, Education and Industry, IDICAIEI 2024

作者： Gurbade, Viraj Vijay Verma, Prateek Gundewar, Swapnil Bramhe, Vijendra Singh Faculty of Engineering and Technology Department of Artificial Intelligence and Data Science Maharashtra Wardha442001 India Faculty of Engineering and Technology Department of Artificial Intelligence and Machine Learning Maharashtra Wardha442001 India Vit Bhopal University School of Computing Science and Engineering Kothrikalan Madhya Pradesh Sehore466114 India

ISBN: (纸本)9798331528713

A data lake is a centralized repository where you may store both structured and unstructured data at any scale. Unlike typical data warehouses, which need data structure for storage, data lakes allow you to store raw data in its natural state. This adaptability makes data lakes suitable for storing large volumes of various data kinds, including photographs, videos, log files, sensor data, and others. When integrating a data lake with Power Business intelligence (BI), organizations can harness the power of both technologies to gain valuable insights from their data. In the realm of data-driven decision-making, organizations face the challenge of managing and extracting insights from vast and diverse datasets, a task conventional on-premises storage solutions often struggle with due to the exponential growth of data. Thus, the adoption of more scalable and flexible alternatives becomes imperative. Cloud-based data lakes emerge as transformative solutions, offering unparalleled scalability, accessibility, cost-effectiveness, and advanced security protocols. This paper discuss the concept of constructing a cloud-based data lake optimized for Power BI, leveraging leading cloud storage services like Azure Data Lake Storage (ADLS) and Amazon S3. By seamlessly integrating Power BI with the cloud data lake, organizations can overcome traditional storage limitations and reap numerous benefits. The practical applications in advanced analytics, customer insights, IoT data management, and data discovery underscore the real-world utility of cloud data lakes. The integration of Power BI with a cloud data lake augments efficient data analysis and self-service analytics potential, offering organizations a comprehensive solution for deriving actionable insights from their data assets. © 2024 IEEE.

关键词： Decision making

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Edge Computing: An Overview 14

Edge Computing: An Overview

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

作者： Shettar, Girish B. Devangavi, Anil D. Basaveshwar Engineering College Department of Information Science and Engineering Bagalkote India Basaveshwar Engineering College Department of Artificial Intelligence & Machine Learning Bagalkote India

In current era, the Edge computing archetype has captured substantial recognition in various educational universities and IT sector. Edge computing assists as an opportunity and act as a key empowering influence for different technological innovation like Augmented Reality (AR), 5th Generation Mobile Network (5G), communications between vehicles (V2V) by interfacing amenities or services at the cloud, Internet of Things (IoT) and providing resources to end clients. The Edge computing architype anticipates to reduce latency, faster digital experiences, location mindfulness backing to time-delicate applications such as voice over IP, live streaming videos, multimedia teleconferencing. This paper comprehensively presents the concepts of the edge computing and in contrast to the cloud computing. Then succinctly describe the edge computing, challenges faced in edge computing, and finally windup with the solutions and various edge computing applications. © Grenze Scientific Society, 2023.

关键词： Internet of things

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Using Petri Nets as an Integrated Constraint Mechanism for Reinforcement learning Tasks

Using Petri Nets as an Integrated Constraint Mechanism for R...

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IEEE Symposium on Intelligent Vehicle

作者： Timon Sachweh Pierre Haritz Thomas Liebig Faculty of Computer Science Chair of Artificial Intelligence TU Dortmund University Dortmund Germany Lamarr Institute for Machine Learning and Artificial Intelligence Dortmund Germany

ISBN: (数字)9798350348811

ISBN: (纸本)9798350348828

The lack of trust in algorithms is usually an issue when using Reinforcement learning (RL) agents for control in real-world domains such as production plants, autonomous vehicles, or traffic-related infrastructure, partly due to the lack of verifiability of the model itself. In such scenarios, Petri nets (PNs) are often available for flowcharts or process steps, as they are versatile and standardized. In order to facilitate integration of RL models and as a step towards increasing AI trustworthiness, we propose an approach that uses PNs with three main advantages over typical RL approaches: Firstly, the agent can now easily be modeled with a combined state including both external environmental observations and agent-specific state information from a given PN. Secondly, we can enforce constraints for state-dependent actions through the inherent PN model. And lastly, we can increase trustworthiness by verifying PN properties through techniques such as model checking. We test our approach on a typical four-way intersection traffic light control setting and present our results, beating cycle-based baselines.

关键词： Privacy Process modeling Petri nets Process control Reinforcement learning Communication channels Production

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A Novel Privacy-Centric Training Routine for Maintaining Accuracy in Traditional machine learning Systems 7th

A Novel Privacy-Centric Training Routine for Maintaining Acc...

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7th International Conference on Information and Communication Technology for Intelligent Systems, ICTIS 2023

作者： Haritas, Hrishikesh K. Haritas, Chinmay K. Kallimani, Jagadish S. Department of Artificial Intelligence and Machine Learning M S Ramaiah Institute of Technology Bengaluru India Undergraduate Programme Indian Institute of Science Bengaluru India

ISBN: (纸本)9789819940394

The increasingly prevalent concerns on user privacy and user’s abstinence on data sharing assuredly spells a decline in novel model development for machine learning. On-Device AI is an exciting paradigm with local training and no requirements of externalizing personal data. Hybridizing these techniques of traditional data upload and server-side training to maximize accuracy and an On-Device approach, protecting privacy standards yields us a novel training routine, which can find itself extensively invoked with increasing privacy concerns. We enumerate multiple use cases in addition to examining feasibility and an abstract implementation of this concept. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Data acquisition

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A Patient-Centric Blockchain Framework for Transparent and Secure Medical Data Sharing using Modified AES 2

A Patient-Centric Blockchain Framework for Transparent and S...

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2nd IEEE International Conference on Integrated intelligence and Communication Systems, ICIICS 2024

作者： Pawar, Priyanka Pramod Kumar, Deepak Meesala, Mohan Kumar Pareek, Piyush Kumar Addula, Santosh Reddy Shwetha, K.S. Department of Information Technology University of the Cumberlands WilliamsburgKY United States Department of Artificial Intelligence & Machine Learning Nitte Meenakshi Institute of Technology Bengaluru India Department of Computer Science and Engineering Dayananda Sagar College of Engineering Bangalore India

ISBN: (纸本)9798331504960

Due to its decentralised, accessible, and secured structure, blockchain-a framework that has historically been viewed with suspicion-has developed into a groundbreaking breakthrough. Reliable automated scripting and static data records are also a part of this system. The intricacy and expertise required to operate with blockchain technology is one of its disadvantages. machine learning (ML) frameworks are the key fixing this problem. It has the potential to make Blockchain more user-friendly. In this research paper, we reviewed the current state of blockchain-based cloud computing, machine learning, and safe data exchange. In this study, we suggest using a blockchain technology to provide a safe space where health entities can share medical information transparently. A modified advanced encryption standard (MAES) based blockchain encryption model is developed by us. This method is based on engineering and AI principles, and its main objective is to design useful artefacts that improve their respective domains. © 2024 IEEE.

关键词： Electronic health record

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Crop Monitoring System with Water Moisture Level using Arduino 5

Crop Monitoring System with Water Moisture Level using Ardui...

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5th International Conference on Smart Systems and Inventive Technology, ICSSIT 2023

作者： Sowmia, K.R. Prithi, S. Vijay, K. Berna, I. Eugene Bhuvaneswaran, B. Department of Artificial Intelligence and Machine Learning Rajalakshmi Engineering College Chennai India Department of Computational Intelligence School of Computing Srm Ist Kattankulathur Campus India Department of Computer Science and Engineering Rajalakshmi Engineering College Chennai India

ISBN: (纸本)9781665474672

Many factors-including sunlight, water, soil, and pest control-need to be considered when cultivating crops. The farmer has other responsibilities besides keeping an eye on the seedlings. In this way, the productivity of farming is enhanced by a machine that tends to all the plants with minimal human intervention. The amount of water a plant receives should be proportional to the amount of sunlight it gets. This research work employs the idea of IoT to connect the monitored plant to the network and transmit data. The microcontroller is an Arduino UNO, and components include a photoresistor for light detection and a moisture sensor for measuring the amount of moisture in the soil. The information is transmitted over a network, and the result is viewed using a mobile application. The data that has been collected is displayed in the app. It warns the user when the plant is in danger of drying out due to a lack of water or sunlight. If the crop is dry, a motordriven water pump is used to saturate the soil, and if it's too wet, a motor-operated shutter opens to let the water out. This project intends to make farming as mechanized and labor-free as possible. © 2023 IEEE.

关键词： Crops

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Exploring PM2.5 Air Pollution and Its Impact on Health Using machine learning 5th

Exploring PM2.5 Air Pollution and Its Impact on Health Using...

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5th International Symposium on Signal and Image Processing, ISSIP 2024

作者： Pati, Alok Kumar Tripathy, Alok Ranjan Tripathy, Alakananda Department of Computer Science Ravenshaw University Cuttack India Department of Computer Application Siksha ‘O’ Anusandhan Deemed to be University Odisha Bhubaneswar India Centre for Artificial Intelligence and Machine Learning Siksha ‘O’ Anusandhan Deemed to be University Odisha Bhubaneswar India

ISBN: (纸本)9789819795147

The increase in vehicle and industrial pollution in air is rising. Air pollution is the major factor driven behind the environmental pollution. An increase in air pollution leads to chronic lung diseases and lower respiratory. In order to check the pollution in time, it is necessary to know about it. Using historical data and machine leaning models, it becomes easier to track the daily increase in air pollution levels. This will help to restrict the air pollution. This paper focuses on the prediction of different pollutants affecting human lungs. Different regression algorithms have been used to predict the amount of particulate matter (PM2.5) in the air which leads to the pollution. This paper focuses on linear regression, ridge regression, lasso regression, support vector regression, and gradient boosting regression for analyzing and predicting air pollution levels. Gradient boosting regression has better accuracy as compared to other regression techniques. Cross-validation is used to ensure the data does not have any outliers. The different regression algorithms have been evaluated using different parameters like root mean squared error (RMSE), R-squared score, mean absolute error (MAE), and mean squared error (MSE). © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Air quality

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