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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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MediGlove: Integrating IoT and machine learning for Personalized Remote Health Care 2

MediGlove: Integrating IoT and Machine Learning for Personal...

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2nd International Conference on Data Science and Information System, ICDSIS 2024

作者： Nanda, K.V. Keerthan Kumar, T.G. Achyutha Prasad, N. Raviprakash, M.L. Anand Babu, J. Naveen Kumar, M.R. Sri Siddhartha Institute Of Technology Master Of Computer Applications Tumkur India Siddaganga Institute Of Technology Information Science And Engineering Tumkur India East West Institute Of Technology Computer Science And Engineering Bangalore India Kalpataru Institute Of Technology Artificial Intelligence And Machine Learning Tiptur India Malnad College Of Engineering Information Science And Engineering Hassan India Vidyavardhaka College Of Engineering Information Science And Engineering Mysore India

ISBN: (纸本)9798350381665

Mobile operating systems have advanced significantly in recent decades, with Android emerging as a widely used open-source platform. Simultaneously, the Internet of Things (IoT) has transformed computer equipment by enabling seamless data transfer over networks without human involvement. This study makes use of intelligent gloves equipped with various sensors, as well as IoT and Android applications, to improve healthcare. In this work, we introduce a pioneering framework that combines IoT and Android technology to revolutionize remote health monitoring called IoT-enabled Smart Gloves for Remote Health Monitoring using an Android-based solution approach (MediGlove). MediGlove uses intelligent smart gloves with robust sensors to continuously collect crucial health data. The solution works with IoT devices to send data quickly to a cloud-based server. The cloud server provides real-time health metric analysis with great precision by utilizing cutting-edge algorithms and machine learning techniques. A simple Android software acts as a dynamic health dashboard, allowing users to track their health in real-time. The framework generates real-time alerts for problematic health markers, allowing prompt medical action. Extensive testing validates the system's efficacy and dependability in health monitoring and the seamless integration of smart gloves with Android applications. © 2024 IEEE.

关键词： Internet of things

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Investigation on Digital Forensic Using Graph Based Neural Network with Blockchain technology 2

Investigation on Digital Forensic Using Graph Based Neural N...

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2nd IEEE International Conference on Data Science and Network Security, ICDSNS 2024

作者： Pawar, Priyanka Pramod Kumar, Deepak Bhujang, Raghavi K. Pareek, Piyush Kumar Manoj, H.M. Deepika, K.S. Information Technology University of the Cumberlands United States Ms Ramaiah Institute of Management Department of Entrepreneurship Bengaluru India Nitte Meenakshi Institute of Technology Department of Artificial Intelligence & Machine Learning Bengaluru India Computer Science and Engineering Bms Institute of Technology and Management Bengaluru India Channabasaveshwara Institute of Technology Gubbi India

ISBN: (纸本)9798350373110

Digital forensics has evolved to focus on leveraging social media evidence, offering substantial support for investigating various crimes. However, scrutinizing social media data for evidence presents significant challenges for authorities. This article proposes employing blockchain infrastructure and natural language processing (NLP) techniques as foundational elements in digital forensic investigations. Specifically, it advocates for a Graph Convolutional Neural Network (GCNN) based approach to multi-class classification. NLP is integral to various stages of this process, including data collection, feature selection, classifier assessment, and vectorization. Moreover, to enhance data security against hackers and network attacks, a blockchain-based system is utilized. Real-world dataset experimentation demonstrates the efficacy of the proposed system. The findings reveal significant improvements in GCNN classification performance when incorporating topology knowledge. Furthermore, optimizing the model necessitates considering various input factors, such as document length, training data representation, and network architecture. This structured approach to digital forensic investigation holds promise for bolstering law enforcement efforts. © 2024 IEEE.

关键词： Graph neural networks

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Hate Speech Detection using CSO based Polynomial Network using Twitter 4

Hate Speech Detection using CSO based Polynomial Network usi...

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4th International Conference on Emerging Research in Electronics, Computer Science and technology, ICERECT 2022

作者： Madhura, G.K. Parameshachari, B.D. Pareek, Piyush Kumar Nitte Meenakshi Institute of Technology Department of Artificial Intelligence and Machine Learning Bengaluru India Nitte Meenakshi Institute of Technology Department of Electronics and Communications Engineering Bengaluru India

ISBN: (纸本)9781665456357

The power of social media as a catalyst for societal transformation is now unrivalled. What happens in one part of the world has repercussions in other parts of the world. This is because the vast quantities of data produced by these platforms may be instantly disseminated to any part of the globe. To make cyber space as welcoming and productive as feasible for all users, developers of these platforms must overcome several obstacles. However, provocative speech and hate speech have emerged as major problems in recent years. The scale of this issue is so large that it cannot be solved by coordinated teamwork alone, no matter how hard people try. Actually, there is a need for the development of an automated approach that can identify and eliminate nasty and insulting remarks before they can do any damage. This paper offers a novel Deep learning-based Hate Speech Detection Scheme (DL-HSDS) to identify hate speech in Twitter data. Even though there are a lot of HSDS methods available, many of them suffer from insufficient feature learning and poor dataset management, both of which negatively impact attack detection precision. Therefore, to improve detection accuracy, the suggested module integrates the Cuckoo Search Optimization algorithm (CSO) with the (SDPN);CSO picks the optimum features in the datasets, and SDPN categorises the data as hate or normal. The suggested model, which employs the tweet text with CSO to imprisonment the tweets' outperforms the previous models. © 2022 IEEE.

关键词： Deep learning

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Development of a Calibration-Free Brain-Computer Interface Utilizing Common Spatial Patterns and artificial Neural Networks for EEG Signal Analysis 6

Development of a Calibration-Free Brain-Computer Interface U...

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6th International Congress on Human-Computer Interaction, Optimization and Robotic Applications, HORA 2024

作者： Cengiz, Korhan Shreyas, J. Udayaprasad, P.K. Gururaj, H.L. Kumar, Dilip S.M. College of Information Technology University of Fujairah United Arab Emirates Faculty of Organization and Informatics University of Zagreb Croatia Croatia Department of Information Technology Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Karnataka Manipal576104 India Department of Artificial Intelligence & Machine Learning Global Academy of Technology Bengaluru India Dept. of Computer Science and Engineering University Visvesvaraya College of Engineering Bangalore India

ISBN: (纸本)9798350394634

A brain computer interface (BCI) system uses a technique known calibration, that takes 20 to 30 minutes to accomplish. For the objective of creating a reliable decoder, the calibration process is challenging and expensive. In order to address the drawbacks of the current system, a spectral-spatial technique has been suggested. The motor imagery (MI) data set, comprising 15 electroencephalography (EEG) signals and fourteen test subjects, is taken into consideration. The two modules are designed to extract characteristics and process data. An artificial neural network (ANN) is used to independently train and test the suggested spectral-spatial algorithm. Based on it, a variety of machine learning techniques, including random forest (RF), neural networks (NN), and XGboost, are used to classify the data, that is then sent to the hidden layer (Lth layer). The obtained results indicates 2% of improvement in comparison with existing methodology. © 2024 IEEE.

关键词： Electroencephalography

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New Approach of artificial intelligence in Digital Forensic Investigation: A Literature Review 1

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International Conference on Advances in Communication technology and Computer engineering, ICACTCE 2023

作者： Verma, Raghav Garg, Sonia Kumar, Karan Gupta, Gaurav Salehi, Waleed Pareek, Piyush Kumar Kniežova, Jaroslava Yogananda School of Artificial Intelligence Computers and Data Science Himachal Pradesh Solan173229 India Mullana Ambala133207 India Department of Artificial Intelligence and Machine Learning Nitte Meenakshi Institute of Technology Bengaluru India Faculty of Management Comenius University in Bratislava 25 Odbojárov 10 Bratislava82005 Slovakia

ISBN: (数字)9783031371646

ISBN: (纸本)9783031371639

A Digital Forensic is a subfield of forensic science known as "digital forensic science" that focuses on the recovery and examination of data from digital devices that are connected to cybercrime. Computer forensics was the original meaning of the word "digital forensics." it has broadened to include any device that could store digital data. National policies on digital forensics didn't start to arise until the beginning of the twenty-first century. The process of locating, protecting, analyzing, and documenting digital evidence is known as digital forensics. When necessary, this is done so that evidence can be presented in court. AI in Digital forensics is one of the technologies that is developing the fastest, and it has had a significant impact on the methods and tools used to examine, monitor, and visualize crime scenes as well as develop effective strategies for dealing with impending threats and attacks on the internet and cyberspace. Modern technology has replaced human labor with machine-oriented designed work that has the capability and ability to minimize error and increase quality, reducing human effort and achieving maximum results with the fewest possible errors. It can influence the outcome and analyze the evidence in the field of digital forensics in a more effective manner to track the outcomes. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： artificial intelligence

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Application of the Semantic Segmentation Method in the Assessment of Forest Carbon Balance 16

Application of the Semantic Segmentation Method in the Asses...

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16th International Conference Management of Large-Scale System Development, MLSD 2023

作者： Tulokhonova, Inna Mikhaylova, Svetlana Danilova, Soelma East Siberia State University of Technology and Management Department of Software Engineering and Artificial Intelligence Ulan-Ude Russia Financial University under the Government of the Russian Federation Department of Data Analysis and Machine Learning Moscow Russia

ISBN: (纸本)9798350337907

This article focuses on the problem of human impact on the natural environment and its solution by machine learning methods. The concept of carbon balance is key in assessing climate change on the planet, which is especially important in connection with global warming. The main causes of global warming are associated with anthropogenic emissions of carbon dioxide as a result of agricultural production, land use and burning of fossil fuels. One of the approaches to mitigate this environmental problem is to estimate the carbon balance based on neural network semantic segmentation of forests of terrestrial ecosystems. The proposed convolutional neural network (CNN) model is based on the U-Net architecture. This architecture surpasses traditional machine learning methods in solving computer vision and pattern recognition problems, and is also quite easy to learn. The input data of the model is a set of satellite images of the forest area. The images were obtained using the Sentinel-2 satellite, which provides high spatial resolution and a large number of spectral channels. A software prototype has been developed to implement the CNN model. Training experiments and calculations on changes in the carbon balance in forests led to the conclusion that the results of the study will contribute to the balanced management of reforestation and carbon deposition. © 2023 IEEE.

关键词： Global warming

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Revolutionising Chrono Nutrition Using AI: A Review for Personalized Dietary Recommendation

Revolutionising Chrono Nutrition Using AI: A Review for Pers...

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Sentiment Analysis and Deep learning (ICSADL), International Conference on

作者： Vidhi Wankhede Meher Langote Prateek Verma Artificial Intelligence and Data Science Faculty of Engineering And Technology Datta Meghe Institute of Higher Education and Research (DU) Sawangi(M) Wardha India Artificial Intelligence and Machine Learning Faculty of Engineering And Technology Datta Meghe Institute of Higher Education and Research (DU) Sawangi(M) Wardha India

ISBN: (数字)9798331523923

ISBN: (纸本)9798331523930

Chrono nutrition is the science of analysing foods' effects and consumption patterns on the outer-infra-clock organism's circadian rhythms. As such, it can provide valuable possibilities for tailored health promotion and disease prevention efforts. With artificial intelligence (AI), there is an opportunity to change dietary advice and make it accurate, dynamic and personalised. This review discusses how AI can improve chrononutrition, analyse large datasets, determine the optimal circadian feeding intervals, and deliver individualised interventions based on chronobiological and genetic data. Limitations include the absence of cross-sectional analysis and inadequate consideration of socio-cultural factors in AI models. AI can significantly contribute to optimising chronotherapeutic or chrono-dietary interventions in metabolic health and disease. Furthermore, a multisectoral approach in which nutritionists, data scientists, and other actors in the healthcare sector must work together is crucial within the present state and future practical application of these advances. In extending the discourse of this paper, it is also essential to establish that much more work needs to be done for AI to make a tangible difference in chronic nutrition and translate it into an optimal solution to the problem of practical dietary advice and disease prevention.

关键词： Analytical models Sentiment analysis Reviews Prevention and mitigation Collaboration Medical treatment Genetics Synchronization artificial intelligence Diseases

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Classifying Parkinson's Disease Using Speech Features

Classifying Parkinson's Disease Using Speech Features

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Intelligent Methods, Systems, and Applications (IMSA)

作者： Hala A. Soliman Mohammed S. Elshourbagi Malak A. Dahy Mena M. Osman Bassant A. Kamal Mohamed Abd Elaziz Nora El-rashidy Faculty of Computer Science and Engineering Galala University Suez Egypt Machine learning and information retrieval-faculty of artificial Intelligence-Kferelshikh University-kaferelshikh

ISBN: (数字)9798350362633

ISBN: (纸本)9798350362640

This scientific paper focuses on the detection of Parkinson's Disease in individuals using vocal/speech features. Specifically, the study records an individual's pronunciation of the short ‘a’ sound (\\ \\\ \\\ae) and applies various Signal Processing Algorithms to process the recorded data. To address the data's class imbalance, both oversampling and undersampling techniques are implemented, and their results are compared. Additionally, a feature selection algorithm is employed to eliminate noise from the dataset while retaining the relevant features necessary for the model. Logistic regression, Support Vector machines (SVM), decision trees, and Ensemble learning methods are utilized to determine the likelihood of an individual having Parkinson's Disease. Furthermore, SHAP (SHapley Additive exPlanations) analysis is conducted to gain a deeper understanding of the impact of features on class determination for each instance. The Random Forest model achieves an accuracy of approximately 95% when trained on the oversampled dataset, surpassing the accuracy obtained from the undersampled dataset. Additionally, a graphical user interface (GUI) is successfully developed to predict the diagnosis of Parkinson's disease.

关键词： Support vector machines Accuracy Parkinson's disease Noise Signal processing algorithms Predictive models Feature extraction

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Incremental learning Framework for Mining Big Data Stream

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Computers, Materials & Continua 2022年第5期71卷 2901-2921页

作者： Alaa Eisa Nora E.L-Rashidy Mohammad Dahman Alshehri Hazem M.El-bakry Samir Abdelrazek Information Systems Department Faculty of Computers and InformationMansoura UniversityMansoura35516Egypt Machine Learning and Information Retrieval Department Faculty of Artificial IntelligenceKafrelsheikh UniversityKafr El-SheikhEgypt Department of Computer Science College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia

At this current time,data stream classification plays a key role in big data analytics due to its enormous *** of the existing classification methods used ensemble learning,which is trustworthy but these methods are not effective to face the issues of learning from imbalanced big data,it also supposes that all data are *** weakness of current methods is that it takes a long evaluation time when the target data stream contains a high number of *** main objective of this research is to develop a new method for incremental learning based on the proposed ant lion fuzzy-generative adversarial network *** proposed model is implemented in spark *** each data stream,the class output is computed at slave nodes by training a generative adversarial network with the back propagation error based on fuzzy bound *** method overcomes the limitations of existing methods as it can classify data streams that are slightly or completely unlabeled data and providing high scalability and *** results show that the proposed model outperforms stateof-the-art performance in terms of accuracy(0.861)precision(0.9328)and minimal MSE(0.0416).

关键词： Ant lion optimization(ALO) big data stream generative adversarial network(GAN) incremental learning renyi entropy

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