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Smart Precision Farming Using Internet of Things and machine learning 1

Smart Precision Farming Using Internet of Things and Machine...

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1st international conference on Cognitive, Green and Ubiquitous computing, IC-CGU 2024

作者： Yadav, Devendra Kumar Das, Subha Ranjan Sahoo, Satyam Barua, Ipsita Sahoo, Dibyam XIM University Computer Science And Engineering Bhubaneswar India

ISBN: (纸本)9798350361780

machine learning (ML) and Internet of Things (IoT) together have a revolutionary impact on agriculture, such as precision farming, which uses cutting-edge technology to address the challenges of unsupervised and random farming practices. Soil testing is a major component of farming that provides knowledge regarding the properties of soil. By measuring the pH level and nutrient deficiencies, the outcome of this test provides knowledge about the fertility and health of the soil. This involves the sampling and testing of each nutrient with certain chemicals, which are further analyzed by experts. This paper presents an innovative framework that harnesses the fusion of IoT and ML to redefine traditional farming methods. In addition, a prototype was created with a combination of various sensing devices, and various analyses of algorithms were performed on the gathered dataset for recommendation. The findings highlight a potential crop suitable for a particular set of soil parameters. © 2024 IEEE.

关键词： Internet of things

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Enhancing Fetal Care and Maternal Health with machine learning: A Systematic Review

Enhancing Fetal Care and Maternal Health with Machine Learni...

引用

2024 IEEE international conference on Intelligent computing and Emerging Communication Technologies, ICEC 2024

作者： Puri, Chetan Reddy, K.T.V. Department of Computer Science and Design Wardha India Department of Artificial Intelligence and Data Science Wardha India

ISBN: (纸本)9798331508432

Fetal growth restriction and preterm delivery proceed to be major around the world wellbeing concerns, with serious consequences for the wellbeing of moms and babies. Provoke and exact estimating of these issues is basic for prompt action. In spite of the fact that progresses have been made in conventional clinical assessment approaches, the potential of computational apparatuses to back clinical decision-making is getting to be progressively clear. With its capacity to assess complex data patterns, machine learning (ML) has gotten to be a reasonable procedure for making strides pre-birth care. The review's conclusions highlight ML's potential to upgrade fetal care. Various examinations yielded empowering discoveries, as machine learning models outperformed routine risk evaluation instruments in terms of anticipated exactness. Numerous pregnancy issues, counting fetal growth limitation and untimely conveyance, have been viably anticipated by machine learning calculations. Healthcare experts can reduce unfavorable results by utilizing focused medicines to distinguish high-risk pregnancies early on. In any case, obstacles and confinements anticipate machine learning from being broadly utilized in restorative settings. Given that solid machine learning models require expansive and representative datasets, information accessibility and quality stay significant concerns. It is vital to ensure information security and take moral issues into consideration. Besides, ML models regularly have destitute interpretability, which makes it challenging to comprehend the basic components directing predictions. Future considers ought to concentrate on making and assessing machine learning models over a extend of demographics, improving the interpretability of models, and incorporating ML into clinical strategies. By handling these issues, machine learning (ML) has the potential to make strides the wellbeing of expectant women and their babies. © 2024 IEEE.

关键词： Obstetrics

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Exploring the Potential of Residential Energy Sharing Communities: A Simulation Study on Load Profile Suitability and Profitability

Exploring the Potential of Residential Energy Sharing Commun...

引用

2024 international conference on Electrical, Electronics and computing Technologies, ICEECT 2024

作者： Panwar, Dheerendra Dodda, Suresh Mahadeva, Rajesh San Jose United States Eudoxia Research Center United States Indian Institute of Technology Department of Polymer and Process Engineering Roorkee India

ISBN: (纸本)9798350378092

The potential of residential energy sharing communities - especially those with photovoltaic and battery storage systems - to facilitate the transition to decentralised, sustainable renewable energy is explored in this study. The study simulates the profitability of 1,000 communities and looks at the relationship between household load profiles and whether or not they are suitable for energy sharing using actual load profile data from USA. To concisely define the high-dimensional load profiles, a comprehensive collection of electricity parameters was built from both a thorough analysis of the literature and new creations. The inability of machine learning (ML) models to predict community revenues effectively, even with efforts in hyperparameteroptimisation and different training dataset configurations, led to recommendations for future research topics. Therefore, a thorough examination of the load profiles of both low- and high-performing communities, theories about the favourable traits of residential load profiles for energy sharing were developed. These theories highlighted the intricate relationship between household energy habits and community energy dynamics. © 2024 IEEE.

关键词： Battery Electricity Energy machine learning Photovoltaic

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Performance Analysis of Online machine learning Frameworks for Anomaly Detection in IoT Data Streams 15

Performance Analysis of Online Machine Learning Frameworks f...

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15th international conference on computing Communication and Networking Technologies, ICCCNT 2024

作者： Ray, Santosh Kumar Susan, Seba Delhi Technological University Department of Information Technology Delhi110042 India

ISBN: (纸本)9798350370249

With the rapid progress of technology, the Internet of Things (IoT) is vital in connecting real-time data sources. These sources generate a substantial volume of streaming data through various applications. When dealing with streaming data, there can be a significant amount of abnormal data, referred to as anomalies, which are completely unknown and can negatively affect the system's performance. In this context, unsupervised anomaly detection methods are expected to perform better than supervised methods. In this paper, we evaluate the performance scores of nine unsupervised anomaly detection models belonging to two recently introduced online machine learning frameworks: River and Python Streaming Anomaly Detection (PySAD). The methodologies investigated in the River framework include Half Space Trees (HST), Quantile Filter One-Class Support Vector machine (QF-OCSVM), Quantile Filter Half Space Trees (QF-HST), and Threshold Filter Half Space Trees (TF-HST). The Histogram-Based Outlier Scores (HBOS), Kitsune's core Algorithms (KitNet), Robust Random Cut Forest (RRCF), Isolation Forest Anomaly Streaming Data (IForestASD), and K Nearest Neighbor Conformal Anomaly Detection (KNNCAD) belong to the PySAD framework. The models are compared in terms of Accuracy, Sensitivity, Specificity, and ROCAUC on the IoTID20 streaming dataset. The paper concludes with a comparative study through performance plots on both frameworks. The River models HST, QF-HST, and TF-HST attained the highest Sensitivity scores of 1.00, whereas only the PySAD model KitNet passed this Sensitivity level. In terms of ROCAUC, the PySAD model IForestASD performs best (0.7937) followed by the River model QF-HST (0.7810), implying that QF-HST performs consistently best among all models. © 2024 IEEE.

关键词： Anomaly detection IoT data streams Online machine learning Unsupervised machine learning

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machine learning for Aggregate computing: a Research Roadmap 42

Machine Learning for Aggregate Computing: a Research Roadmap

引用

42nd IEEE international conference on Distributed computing Systems (ICDCS)

作者： Aguzzi, Gianluca Casadei, Roberto Viroli, Mirko Univ Bologna Alma Mater Studiorum Cesena Italy

ISBN: (数字)9781665488792

ISBN: (纸本)9781665488792

Aggregate computing is a macro-approach for programming collective intelligence and self-organisation in distributed systems. In this paradigm, a single "aggregate program" drives the collective behaviour of the system. provided that the agents follow an execution protocol consisting of asynchronous sense-compute-act rounds. For actual execution, a proper aggregate computing middleware or platform has to be deployed across the nodes of the target distributed system, to support the services needed for the execution of applications. Overall, the engineering of aggregate computing applications is a rich activity that spans multiple concerns including designing the aggregate program, developing reusable algorithms, detailing the execution model, and choosing a deployment based on available infrastructure. Traditionally, these activities have been carried out through adhoc designs and implementations tailored to specific contexts and goals. To overcome the complexity and cost of manually tailoring or fixing algorithms, execution details, and deployments, we propose to use machine learning techniques, to automatically create policies for applications and their management. To support such a goal, we detail a rich research roadmap, showing opportunities and challenges of integrating aggregate computing and learning.

关键词： aggregate computing machine learning multi-agent learning self-organising systems

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Speech Emotion Recognition using machine learning: A Review 14

Speech Emotion Recognition using Machine Learning: A Review

引用

14th international conference on Advances in computing, Control, and Telecommunication Technologies, ACT 2023

作者： Odei-Tettey, Ama Gyekyewaa Yeluripati, Govindha Ramaiah Computer Science Department Lancaster University Ghana Accra Ghana

Affective computing, the area of technology which the topic of this research paper falls under, is concerned with sensing the emotions of users and using the data gathered from the inputs that these users provide to serve them better. Speech Emotion Recognition, or SER for short, has a growing number of applications, including intelligent toys and psychiatric diagnoses. Despite the strides that have been made in this area of computing from its inception in 1996, there is much room for improvement and if these improvements are made, SER could prove useful to recreational users and professionals across industries. This paper aims to comparatively analyze various machine learning approaches to emotion recognition using speech, and to attempt to suggest optimal techniques for use throughout the development process to support the realization of this goal. © Grenze Scientific Society, 2023.

关键词： Toys

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Resolving Design Clashes Using machine learning 15

Resolving Design Clashes Using Machine Learning

引用

15th international conference on computing Communication and Networking Technologies, ICCCNT 2024

作者： Kumar, Abhishek Kumar, Dheeraj Swara, Hemaksh Gupta, Anjana Gupta, Trasha Delhi Technological University Department of Applied Mathematics New Delhi India

ISBN: (纸本)9798350370249

The construction process relies heavily on the collaborative efforts of professionals, including engineers, designers, architects, and project managers, who work together to guide projects from start to finish. This collaborative effort faces numerous challenges, such as inadequate communication among the stakeholders, budgetary constraints, and difficulties in interdisciplinary collaboration. These challenges can often lead to design clashes, where conflicting elements occupy the same space in the design. Addressing these clashes promptly is crucial to minimize rework, enhance efficiency, and ensure the smooth progression of construction projects. Failure to address these clashes in a timely manner can lead to costly rework. This paper proposes a machine learning algorithm to tackle this issue. The proposed algorithm trains multiple bagging classifiers with neural network base models, evaluates their performance, and finally creates a voting classifier using the trained models for ensemble prediction. The proposed algorithm exhibits noteworthy performance in terms of both accuracy and computational efficiency. © 2024 IEEE.

关键词： Back-propagation neural network Building Information Modelling Design Clashes

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Deep learning-based cognitive digital twin system for wrist pulse diagnostic and classification 2nd

Deep learning-based cognitive digital twin system for wrist ...

引用

2nd international conference on Next-Generation computing and Information Systems, ICNGCIS 2023

作者： Chaudhary, Jitendra Kumar Parimalam, T. Alghayadh, Faisal Yousef Keshta, Ismail Soni, Mukesh Degadwala, Sheshang School of Computing Graphic Era Hill University Bhimtal Campus Uttrakhand India Department of Computer Science Nandha Arts and Science College Tamilnadu Erode India Computer Science and Information Systems Department College of Applied Sciences AlMaarefa University Riyadh Saudi Arabia Dr. D. Y. Patil Vidyapeeth Pune Dr. D. Y. Patil School of Science & Technology Tathawade Pune India Department of Computer Engineering Sigma University Gujarat Vadodara India

ISBN: (纸本)9781032738659

In analyzing and recognizing wrist pulse signals, it isn’t easy to mine the nonlinear information of wrist pulse signals using analysis methods such as time and frequency. Traditional machine learning methods require the manual definition of features and cannot perform self-learning of features. A cognitive digital twin technique for pulse analysis and recognition based on threshold-less recursive graph and CNN is proposed. The wrist pulse signal is converted into a threshold-free recursive graph based on the nonlinear dynamics’ theory. The VGG-16 CNN automatically extracts the nonlinear features of the recursive graph, and a pulse condition classification model is established. Experimental finding several that the classification of the proposed method accuracy can reach 98.14%, as compared with the existing pulse classification methods. This study offers a novel concept and strategy for classifying pulse signals, and it has application to the objectification of pulse diagnosis. © 2025 The Author(s).

关键词： Graphic methods

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A machine learning Approach to Model Counting 36

A Machine Learning Approach to Model Counting

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36th international conference on Tools with Artificial Intelligence

作者： Dalla, Marco Visentin, Andrea O'Sullivan, Barry Univ Coll Cork SFI Ctr Res Training AI Sch Comp Sci & IT Cork Ireland Univ Coll Cork Insight Ctr Data Analyt Sch Comp Sci & IT Cork Ireland

ISBN: (纸本)9798331527242;9798331527235

Model counting (#SAT) is the problem of computing the number of satisfying assignments for a given Boolean formula. It has a significant theoretical and practical interest. Tackling it can be challenging since the number of potential solution grows exponentially with the number of variables. Due to the inherent complexity of the problem, approaches to approximate model counting have been developed as a practical alternative. These methods extract the number of solutions within user-specified tolerance and confidence levels and in a fraction of the time required by exact model counters. However, even these methods require extensive computations, restricting their applicability to relatively small instances. In this paper, we propose a new approximate machine learning model counter that overcome this limitation. Predicting the number of solutions can be seen as a regression problem. We deploy an array of machine learning techniques trained to infer the approximate number of solutions based on statistical features extracted from a SAT propositional formula. Extensive numerical experiments performed on synthetic crafted and benchmark datasets show that learning approaches can provide a good approximation of the number of solutions with a much lower computational time and resource cost than the state-of-the-art approximate and exact model counters, making it possible to approximate the model count of instances previously out of reach. We then investigated the structural factors that lead to a high model count using AI explainability approaches.

关键词： Model Counting machine learning SAT Problem Feature Analysis

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Enhancing Cyclone Center Identification in Radar Images through Deep learning and Match Recognition 15

Enhancing Cyclone Center Identification in Radar Images thro...

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15th international conference on computing Communication and Networking Technologies, ICCCNT 2024

作者： Shanmugam, Thirumurugan Chandrashekar, Chirag Subburaj, Maheswari Sivaraman, Arun Kumar Sultana, Ajmery Thangavel, Senthil Kumar University of Technology and Applied Sciences College of Computing and Information Sciences Suhar Oman Vellore Institute of Technology School of Computer Science and Engineering Chennai India Photon Interactive Inc Digital Engineering Solution Center ON Canada Algoma University Brampton School of Computer Science and Technology ON Canada Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350370249

This research focuses on improving the identification of cyclone centers using deep learning and match recognition applied to radar images. Accurately pinpointing the cyclone's center is vital for predicting its intensity and trajectory. However, challenges persist in automatically locating the center due to the diverse nature of cyclone morphology and structure. To address this, the deep convolutional network's capability is leveraged to capture various structural features in images by proposing two-step approach for cyclone center localization. Initially, a pre-trained EfficientDet model is employed using transfer learning to obtain weights. Subsequently, these weights, along with the data, are utilized in a deep learning model to provide precise coordinates of the cyclone center in the respective image. The effectiveness of existing deep learning and machine learning models show that the cyclone prediction systems have an accuracy ranging from 86% to 92% or better, and cyclone eye detection accuracy surpassing 87%. Experiment outcomes indicate that the proposed methodology outperforms conventional methods and existing works, showcasing its potential for enhancing cyclone monitoring and forecasting. © 2024 IEEE.

关键词： Cyclone detection deep learning efficient det and YOLO model transfer learning

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