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Sea Erosion Prediction and Monitoring Using IOT and ML 2

Sea Erosion Prediction and Monitoring Using IOT and ML

2nd International Conference on Recent Trends in Microelectronics, Automation, Computing, and Communications Systems, ICMACC 2024

作者： Usha Bala, V. Harshavardhan Sai, Y. Slipa Shramila, V. Asritha, M. Bharath, M. Chandu, A. Anil Neerukonda Institute of Technology & Sciences Dept. of Computer Science & Engineering Visakhapatnam India

ISBN: (纸本)9798350366570

One of the major environmental issues that the world is currently facing is sea erosion, which requires an innovative solution for early detection and mitigation. Our paper focuses on the integrated system that combines both IoT and Machine Learning. The four main important environmental factors that are responsible for sea erosion are temperature, wave height, wave speed, and soil moisture. IoT sensors deployed along the prototype design are used to monitor these environmental parameters. Another key component is the machine learning algorithm, which is used analyse the interactions, patterns, and relations among the environmental data. By analysing the patterns among the previous data, the model can predict the likelihood of sea erosion events. This allows for the early detection of erosion events and timely interventions that can minimize the damage or loss caused by sea erosion. The main aim of this project is to help the coastal management authority take smart decisions and proactive steps using both real-time and past data analysis. © 2024 IEEE.

关键词： Soil erosion

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A PAC-Bayesian Framework for Optimal Control with Stability Guarantees 63

A PAC-Bayesian Framework for Optimal Control with Stability ...

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63rd IEEE Conference on Decision and Control, CDC 2024

作者： Boroujeni, Mahrokh Ghoddousi Galimberti, Clara LucIa Krause, Andreas Ferrari-Trecate, Giancarlo Institute of Mechanical Engineering Epfl Switzerland Eth Zϋrich Department of Computer Science Switzerland

ISBN: (纸本)9798350316339

Stochastic Nonlinear Optimal Control (SNOC) involves minimizing a cost function that averages out the random uncertainties affecting the dynamics of nonlinear systems. For tractability reasons, this problem is typically addressed by minimizing an empirical cost, which represents the average cost across a finite dataset of sampled disturbances. However, this approach raises the challenge of quantifying the control performance against out-of-sample uncertainties. Particularly, in scenarios where the training dataset is small, SNOC policies are prone to overfitting, resulting in significant discrepancies between the empirical cost and the true cost, i.e., the average SNOC cost incurred during control deployment. Therefore, establishing generalization bounds on the true cost is crucial for ensuring reliability in real-world applications. In this paper, we introduce a novel approach that leverages PAC-Bayes theory to provide rigorous generalization bounds for SNOC. Based on these bounds, we propose a new method for designing optimal controllers, offering a principled way to incorporate prior knowledge into the synthesis process, which aids in improving the control policy and mitigating overfitting. Furthermore, by leveraging recent parametrizations of stabilizing controllers for nonlinear systems, our framework inherently ensures closedloop stability. The effectiveness of our proposed method in incorporating prior knowledge and combating overfitting is shown by designing neural network controllers for tasks in cooperative robotics. © 2024 IEEE.

关键词： Stochastic systems

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Multi-Lingual Semantic-Based Sentiment Analysis Using Generative AI

Multi-Lingual Semantic-Based Sentiment Analysis Using Genera...

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2024 International Conference on computer, Electronics, Electrical engineering and their Applications, IC2E3 2024

作者： Biswas, Praloy Arockiam, Daniel Neogi, Subhrendu Guha Sengupta, Somenath Saraswat, Sanjeev Chanda, Rishit Amity University Madhya Pradesh Department of Computer Science and Engineering Madhya Pradesh Gwalior India The Neotia University Department of Computer Science and Engineering West Bengal Sarisha India Heritage Institute of Technology Department of Computer Science and Engineering West Bengal Kolkata India Institute of Business Management GLA University Mathura India SRM Institute of Science and Technology Faculty of Engineering and Technology Tamilnadu Chennai India

ISBN: (纸本)9798350388534

Generative AI models have extensive applications in Natural Language Processing (NLP) and large language models (LLMs) across various domains. They demonstrate high performance in numerous NLP tasks such as summarization, language understanding, reasoning, language generation, question-answering, sentiment categorization, and translation. Newer LLMs are projected to analyse and generate text in multiple languages because to their training on multilingual datasets, like ChatGPT, BLOOMZ, and other similar versions. Considering how frequently LLMs are used, it is critical to assess their effectiveness in multilingual environments. A major issue to be noted is that in a zero-shot context, the present generative models are not very good at producing text in Indian languages. Several scholarly articles studied generative LLMs in the English language. In contrast, LLM for Indic languages is not meant to be used in a zero-shot manner in downstream applications due to poor generating performance. Generative LLMs evaluate models on standard NLP benchmarks covering NLP datasets in diverse languages. We compared the performance of generative LLMs with non-autoregressive models on these tasks to evaluate the generative AI model's performance compared to the previous generation of LLMs. A framework for evaluating generative LLMs in the multilingual setting has been evaluated to provide future directions. © 2024 IEEE.

关键词： Generative adversarial networks

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Sustainable Solutions: Enhancing Tree Enumeration for Informed Land Use Decisions 3

Sustainable Solutions: Enhancing Tree Enumeration for Inform...

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3rd International Conference for Advancement in Technology, ICONAT 2024

作者： Sricharitha, Podduturi Prajwala, Pola Sai Manikanta, Polina Pranav Sowmya, Nenavath Sri Rajyalakshmi, Ch. B.V.Raju Institute of Technology Computer Science and Engineering Medak Narsapur India

ISBN: (纸本)9798350354171

Forest land is being diverted due to the rising need for resource extraction and land development, which poses significant global risks to ecological balance and biodiversity. The quick diversion of forest land demands efficient tree enumeration techniques. The accuracy and time-consuming nature of traditional tree enumeration methods underscores the need for better, more innovative approaches. Advanced computer vision technologies have the potential to improve environmental monitoring and management in the field of forest conservation. This paper investigates how these advanced technologies, particularly the YOLO (You Only Look Once) framework, can be used to improve and facilitate the tree enumeration process, incorporating object detection methodologies. An enhanced model for effectively detecting tree populations in forested areas can be developed by utilizing neural network architectures like YOLOv8, the most recent version of the YOLO framework. The integration of deep learning algorithms within the YOLOv8 framework facilitates accurate tree detection and enumeration in forest land areas. The proposed model based on YOLOv8 achieved an accuracy of 98.37 percent, demonstrating the potential of this approach in enhancing precision and efficiency in tree enumeration. By enabling informed decisions on sustainable land use and conservation, our proposed methodology significantly contributes to environmental monitoring and management efforts. © 2024 IEEE.

关键词： Forest ecology

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Using Recurrent Neural Networks to Forecast Climate Change: A Time Series Analysis of Global Temperature Variability 1

Using Recurrent Neural Networks to Forecast Climate Change: ...

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1st International Conference on Advances in computer science, Electrical, Electronics, and Communication Technologies, CE2CT 2025

作者： Rathore, Nitasha Anubha Singh, Kuljeet Kaur, Gaganpreet Sharma, Chetna Sinha, Anurag Bharatiya Vidyapeeth College of Engineering Department of Computer Science and Engineering New Delhi India KIET Group of Institutions India School of Sciences Christ University Department of Computer Science Delhi NCR India Chitkara University Institute of Engineering and Technology Chitkara University Punjab India IGNOU Department of Computer science New Delhi India

ISBN: (纸本)9798331518578

Predicting the upcoming weather instances is very crucial. It depends on different climatic parameters like humidity, pressure, temperature, etc. In this paper, the historical data of the weather in the India area is used for future weather instances of the India for farmers' convenience in terms of the agricultural instance which depends on the weather and, functioning according to that which will restore the energy. For weather forecasting we have use the machine learning algorithm and probabilistic predictions of the predictive analytics based on soft computing and. NGBoost algorithm and. Linear models of the machine learning for predictive. Comparative weather incidence spaced on the historical data. We have also used, sliding window algorithm of the statistics for predicting the ideology of the concept of different contrasted windows and year. We've also used utility thirds and machine learning algorithm, classified for predicting the weather based on different features The overall implementation in this paper, shows the accuracy which we have gathered from the data set. An implementation of algorithm which ranges between 80% to 90% and the entire algorithm have been compared based on the feature instances. Work can be concluded on the measurement of the algorithm, which we have got after the implementation of Models. Which rely upon the different data features and thus it can be beneficial for preserving the energy and materials in the India agriculture area and forecasting the weather as per day Agricultural conditions. © 2025 IEEE.

关键词： Adversarial machine learning

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Exploring News-Feed Credibility using Emerging Machine Learning and Deep Learning Models

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Journal of engineering science and Technology Review 2022年第3期15卷 31-37页

作者： Aju, D. Kumar, Kakelli Anil Lal, Anisha M School of Computer Science and Engineering Vellore Institute of Technology TN Vellore India

In the recent past, the phenomenal growth, availability, and access of information on social media have made it perplexing to discern between real and fake information. The faster and easier dissemination of information through various means has accelerated the explosive growth of its falsehood. At the same time, the newsfeeds and their credibility in the social networks are in danger since the fake news is alarmingly disseminating very fast. Henceforth, the credibility of the newsfeeds or any information has become a real research challenge to cross-check the respective news or information. The cross verification can be performed concerning its source, the exact content, and the respective publisher to catalog it into fact or fake. Despite a few constraints, machine learning plays a crucial and significant role in classifying the respective news feeds. Various machine learning methodologies such as BLR (Bilinear logistic regression), NB (Naive Bayes), SVM (Support Vector Machine), and RF (Random Forest) have been reviewed and experimented with for detecting the fact and fake news feeds. After the experimentation, the limitations of the respective machine learning methods were explored and noted. Henceforth, a deep learning method called BERT is implemented and it is observed that BERT provides better efficiency than the machine learning algorithms. Furthermore, it is ascertained that the deep learning method provided the best accuracy of 99.79 % with the available dataset. © 2022. School of science, IHU. All rights reserved.

关键词： Support vector machines

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Real-Time Crop Analysis with Enhanced Image Classification Using Deep Learning and Fuzzy Logic Techniques 2

Real-Time Crop Analysis with Enhanced Image Classification U...

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2nd International Conference on computer Vision and Internet of Things, ICCVIoT 2024

作者： Kanimozhi, S. Deepika Roselind, J. Shah, Neal Sai Ramesh, L. School of Computer Science and Engineering VIT University Chennai India School of Electronics Engineering VIT University Chennai India Department of Computer Science and Engineering St. Joseph's Institute of Technology Chennai India

ISBN: (纸本)9781837242672

The proposed research presents a smart irrigation system is designed to optimize agricultural water management using a robotic framework that integrates deep learning and fuzzy logic for real-time crop classification and decisionmaking. It employs a combination of VGG-19 and a fusion model RTCropNet (Real-time crop network), leveraging the strengths of each architecture: VGG-19 for robust feature extraction and the RTCropNet fusion for enhanced spatial localization and fine-grained crop classification. Pre-processing is performed through advanced fuzzy logic techniques to improve image quality, enhancing classification accuracy across diverse crop types. Evaluation metrics indicate that the RTCropNet model achieves a training accuracy of 80.15% and a validation accuracy of 81.77%, with precision, recall, and F1 scores of 0.83, 0.82, and 0.82, respectively, outperforming traditional methods on complex agricultural imagery. Comparative analysis shows the model's superiority over EfficientNet-B3, DenseNet-121, and MobileNetV2, particularly in handling high-resolution spatial details, while maintaining efficiency in real-time conditions. This integration of deep learning with fuzzy logic and sensor data represents a novel approach in precision irrigation, offering a dynamic, datadriven response system that adjusts irrigation based on real-time environmental conditions, distinguishing it from conventional SOTA approaches. © The Institution of engineering & Technology 2024.

关键词： Deep learning

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Advancing Spelling Correction through Natural Language Processing and TextBlob: A Context-Aware Approach

Advancing Spelling Correction through Natural Language Proce...

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2023 International Conference on the Confluence of Advancements in Robotics, Vision and Interdisciplinary Technology Management, IC-RVITM 2023

作者： Singh, Nongmeikapam Thoiba Mishra, Aditya Mohan Singh, Ankit Chanu, Asem Debala Chandigarh University Department of Computer Science and Engineering Punjab India Indian Institute of Information Technology Department of Computer Science and Engineering Assam Guwahati India

ISBN: (纸本)9798350306088

This study explores the development of an advanced spelling correction system using Natural Language Processing (NLP) and the TextBlob library. It addresses the prevalent issue of spelling errors in written text and aims to create a system that not only corrects typographical errors but also considers the contextual nuances in which words are used. The paper begins by explaining key NLP concepts, including tokenization, part-of-speech tagging, and contextual analysis, which form the foundation of the spelling correction process. These techniques are crucial for systematically identifying and rectifying spelling errors. Furthermore, the research focuses on integrating TextBlob's dictionary-based correction mechanism with contextual awareness to improve precision, particularly in context-dependent correction scenarios. The study rigorously evaluates the system's performance through real-world testing and transparently discusses potential limitations and challenges encountered during development. Overall, the research paper highlights the transformative impact of NLP and TextBlob on spelling correction, demonstrating substantial improvements in correction accuracy and contextual comprehension. It emphasizes the wide-ranging applications of this technology, extending its influence beyond spelling correction to various domains, including word processing software, search engines, and virtual assistants. The findings endorse the effectiveness of this approach and call for continued research and development efforts to advance language processing technologies and enhance written communication across diverse applications. © 2023 IEEE.

关键词： Machine learning

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Enhancing Breast Cancer Classification through Attention Based VGG-19 and Federated Learning with Multi-Center Medical Imaging 5

Enhancing Breast Cancer Classification through Attention Bas...

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5th IEEE International Conference on Communication, Computing and Industry 6.0, C2I6 2024

作者： Valarmathi, C. Thangaraj, S. John Justin Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350379884

Breast cancer remains the primary cause of death for women worldwide, which emphasises how important it is to detect the disease early and accurately in order to increase survival rates. Conventional approaches encounter substantial challenges since large labelled datasets are required, and they are frequently limited by privacy issues and dispersed data sources. This work introduces an novel method to improve breast cancer classification by combining Attention-based VGG-19 with Federated Learning in multi-center medical imaging. Accurate breast cancer detection relies on analyzing medical images such as mammograms, MRIs, and biopsies, which often vary across different healthcare centers. The proposed model uses VGG-19, a deep convolutional neural network, enhanced with an attention mechanism to highlight key regions in the images for more precise classification. Federated Learning is also integrated, enabling multiple medical institutions to collaboratively train the model without sharing sensitive patient data, thereby preserving privacy and data security. This approach allows the model to learn from diverse datasets while keeping the data localized. The synergy of attention-based VGG-19 and Federated Learning enhances the accuracy, robustness, and generalization of breast cancer classification, offering a promising solution for early detection and diagnosis across multiple medical centres. © 2024 IEEE.

关键词： Convolutional neural networks

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Harnessing the Potential of the Internet of Things: Navigating Choices Between Fog and Cloud Infrastructure in Agriculture 1

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2nd International Conference on Cyber Intelligence and Information Retrieval, CIIR 2023

作者： Singh, Malikhan Arora, Kavita Pathak, Sonal Quraishi, Suhail Javed Singh, Anupam Ather, Danish Computer Science and Engineering Modern Institute of Technology and Research Centre Rajasthan Alwar India School of Computer Applications Manav Rachna International Institute of Research and Studies Haryana Faridabad India Department of Computer Science and Engineering Graphic era Hill University Dehradun India Amity University in Tashkent Tashkent Uzbekistan

ISBN: (数字)9789819776030

ISBN: (纸本)9789819776023

In recent years, the Internet of Things (IoT) has become a pivotal force in transforming various sectors, with agriculture being a prominent beneficiary. The integration of smart devices, sensors, and data-driven decision-making tools has led to the emergence of a more efficient and sustainable farming landscape. Central to this transformation is the decision of where and how to process and store the vast amounts of data generated by these devices. This paper delves into the practical implications of fog and cloud computing systems in the agricultural domain taking Delhi NCR and region as a sample area by considering factors like efficiency, scalability, and real-world challenges faced by farmers. The aim here is to provide a comprehensive guide to stakeholders in making an informed choice between fog and cloud infrastructure for their IoT-enabled agricultural endeavors. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Smart agriculture

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