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Advancements in Smart Agriculture: A Comprehensive Review of Machine learning and IOT Approaches

Advancements in Smart Agriculture: A Comprehensive Review of...

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2024 international conference on Communication, computing and Internet of Things, IC3IoT 2024

作者： Shanthakumari, G. Vignesh, A. Harish, R. Roshan Karthick, R. Sri Sairam Engineering College Department Of Electronics And Communication Engineering Chennai India

ISBN: (纸本)9798350352689

In contemporary agriculture, optimizing crop yield necessitates efficient weed management and precise irrigation. This review amalgamates insights from ten studies, centering on the integration of machine learning, deep learning, and IoT technologies to address these challenges. The weed detection segment underscores advanced solutions like attention-based deep networks and visual transformers, tackling crop-weed discrimination challenges, albeit contending with issues such as high computation costs. Transitioning to precision agriculture, the paper explores machine and deep learning techniques for superior weed identification and control, offering viable integration with herbicide applicators. Challenges encompass interoperability and standardization of sensing and control protocols. The narrative then shifts to smart irrigation systems, presenting IoT-based solutions for real-time monitoring and automatic irrigation. Systems like Arduino Mega 2560 and dynamic irrigation scheduling boost crop productivity, with advantages including farmer-friendly interfaces and limitations in potential implementation costs on larger scales. In the results analysis, a collaborative impact is evident, envisioning a seamless integration of advanced technologies for sustainable and productive agriculture. Collectively, these studies signal a promising trajectory in leveraging technology to address challenges, paving the way for a new era of precision farming amid the evolving agricultural landscape. © 2024 IEEE.

关键词： Irrigation

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An algorithm for recommendations that fuses graph representation learning and matrix factorization 7

An algorithm for recommendations that fuses graph representa...

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7th international conference on Advanced Electronic Materials, Computers, and Software engineering, AEMCSE 2024

作者： Zhao, Bowen Dong, Baoliang Wang, Kai Liu, Zihao Department 4 of System North China Institute of Computing Technology Beijing100083 China

ISBN: (纸本)9781510681866

Compared to conventional methods of collaborative filtering for recommendations, algorithms that employ matrix factorization are adept at tackling the challenge of sparse data and enhancing the performance of recommendation processes. However, matrix factorization algorithms still have the drawback of not fully utilizing user feature information. To address this, we propose the TransH-MF model. This approach incorporates graph representation learning techniques grounded in matrix factorization. Initially, the TransH graph representation learning algorithm is utilized to project both entities and their relationships into a space, whose vector dimension is lower. Subsequently, the derived vectors are employed to assess the semantic proximity between entities. Ultimately, this proximity data is merged into the process of solving the optimal matrix factorization result. Experiments on the more recent public movielens-latest dataset have proven that the TransH-MF model outperforms the single matrix factorization in regard to both MAE and RMSE metrics. © 2024 SPIE.

关键词： Knowledge graph

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Pre-trained InceptionV3 for Efficient Tuberculosis Detection 15

Pre-trained InceptionV3 for Efficient Tuberculosis Detection

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

作者： Ahuja, Vandana Vats, Satvik Sharma, Rishabh Swami Vivekanand Institute of Engineering and Technology Department of Computer Science & Engineering Punjab Ramnagar India Computer Science and Engineering Graphic Era Hill University Dehradun248002 India Graphic Era Deemed to be University Dehradun248002 India Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura India

ISBN: (纸本)9798350370249

Tuberculosis (TB) continues to be a health hazard globally, especially in the developing world, thus requiring effective diagnostic techniques. This research focuses on the utilization of the transfer learning InceptionV3 model for the identification of the disease from Chest X -ray inpatients. InceptionV3 is fine-tuned here and is a CNN model known to produce high accuracy in image classification tasks particularly TB and non-TB chest X-ray images dataset. Based on the above objectives, the study intends to assess the accuracy of the proposed model by comparing the values of sensitivity, specificity, and the overall accuracy of the model. Real-life proof signifies that the fine-tuned model named InceptionV3 can be effective in diagnosing TB with negligible error levels, a definite bonus if compared with molecular systems that can misdiagnose close to a third of the cases. Overall, the study findings indicate that fuelling the current diagnostic algorithms with InceptionV3 strengthens the probabilities of timely detection and treatment of TB thereby improving the patient's prognosis and curtailing the spread of TB. His study highlights the possibility of the utilization of advanced deep learning approaches in an attempt to solve pertinent problems relating to diagnosis in the medical field. © 2024 IEEE.

关键词： covid-19 deep learning inceptionV3 pneumonia Tuberculosis

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A Combined Deep learning Approach for Pusa Gourd Classification in India 15

A Combined Deep Learning Approach for Pusa Gourd Classificat...

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

作者： Sood, Prince Vats, Satvik Ahuja, Vandana Sharma, Rishabh Swami Vivekanand Institute of Engineering and Technology Departure of Computer Science & Engineering Punjab Ramnagar India Graphic Era Hill University Graphic Era Deemed to be University Computer Science and Engineering Dehradun248002 India Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura India

ISBN: (纸本)9798350370249

Classification of crops is vital for improving yields and provision of food items through agriculture. In India particularly, the identification of bottle gourds, with more emphasis on varieties like Pusa Samridhi, Pusa Naveen, Pusa Meghdoot, and Hybrid 1 is of much value to the farmers and agricultural scientists. This paper presents a new combined deep learning model for these bottle gourd varieties classification applying convolutional neural network (CNN) and recurrent neural network (RNN) deep learning models for the best accuracy. The CNN component is employed to extract features from the images of the bottle gourds in terms of the pattern and the texture of the skin while the RNN is used in the processing of the features to model the temporal analysis and sequence data for better classification results. The proposed CNN-RNN hybrid model shows enhanced accuracy in identifying all the various kinds of bottle gourd, which is quite higher than the conventional methods of classification. This work is a major contribution to the agricultural technology domain, as it presents a practical and accurate approach for crop classification that can be applied to other types of crops, thereby contributing to the development of precision agriculture and enhancement of crop selection in India. © 2024 IEEE.

关键词： Agricultural technology Bottle gourd Convolutional Neural Networks India Variety classification

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Federated learning for Enhanced Malware Threat Detection to Secure Smart Power Grids 16th

Federated Learning for Enhanced Malware Threat Detection to ...

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16th international conference on Ubiquitous computing and Ambient Intelligence

作者： Shafi, Saira Tariq, Noshina Khan, Farrukh Aslam Ali, Aftab Air Univ Dept Avion Engn Islamabad Pakistan King Saud Univ Ctr Excellence Informat Assurance Riyadh 11653 Saudi Arabia Ulster Univ Sch Comp Belfast BT15 1ED Antrim North Ireland Natl Univ Comp & Emerging Sci Dept Artificial Intelligence & Data Sci Islamabad Pakistan

ISBN: (纸本)9783031775703;9783031775710

Cyber-Physical Systems (CPS) and the Internet of Everything (IoE) are vital in improving the efficiency of contemporary structures like Smart Grids by supporting the interconnection of people, processes, data, and things. Due to the convergence of devices and systems required by the IOE, smart grid has to be equipped with highly effective and reliable methods of attack detection. This research introduces a novel federated learning approach that utilizes the "CIC-MalMem-2022" dataset to enhance malware detection through a two-layered framework. The CPS layer focuses on local model training, while the Fog layer integrates thesemodels into a global framework. The results demonstrate that the global model achieves superior performance with an accuracy of 99% and a precision of 99% on the training dataset, compared to the local model's 85% accuracy and 75% precision on the test dataset. Additionally, the global model maintains 88% accuracy and 78% precision on the test set, underscoring the efficiency of the federated learning approach.

关键词： Smart Grids Cyber-Physical Systems Federated learning Malware Machine learning

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Learner's Changes in the Number of Solved Problems and Impression of Collaborative learning with a Robot Using the Problem-Number Negotiation Method 13

Learner's Changes in the Number of Solved Problems and Impre...

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Joint 13th international conference on Soft computing and Intelligent Systems and 25th international Symposium on Advanced Intelligent Systems, SCIS and ISIS 2024

作者： Kaede, Hiroki Kono, Taro Jimenez, Felix Miyamoto, Tomoki Graduate School of Information Science and Technology Aichi Prefectural University Nagakute-shi Aichi Japan Graduate School of Informatics and Engineering The University of Electro-Communications Tyo-hu-shu Tokyo Japan

ISBN: (纸本)9798350373332

In recent years, educational-support-robots have attracted considerable attention. In conventional collaborative learning with robots, the number of problems to be answered by the learner (the number of solved problems) is predetermined. This approach precludes learners from engaging in self-directed learning, where they are able to determine the number of solved problems independently. As an alternative, previous studies proposed the Problem-Number Negotiation Method, which involves a dialogue between the learner and the robot prior to the commencement of learning. Previous studies have indicated that short-term collaborative learning with a robot using the Problem-Number Negotiation Method (proposed robot) has the potential to enhance learners' abilities to improve the number of solved problems. However, the previous studies have not verified the changes in the number of solved problems and the impression of the robot in long-term collaborative learning. This paper examines the changes in the number of solved problems and the impression of the robot on university students during long-term collaborative learning. The experimental results found that the proposed robot can spontaneously answer a larger number of solved problems than the conventional robot even during long-term collaborative learning. Moreover, the proposed robot gave the same impressions as the conventional robot. © 2024 IEEE.

关键词： Adversarial machine learning

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Accurate Estimation Technique Development for Second Hand Car Through Linear Regression Algorithm 5

Accurate Estimation Technique Development for Second Hand Ca...

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5th IEEE international conference on computing, Power, and Communication Technologies, IC2PCT 2024

作者： Jain, Satwik Punia, Sanjeev Kumar Galgotias University School of Computing Science & Engineering Greater Noida India

ISBN: (纸本)9798350383522

Car production volumes have increased dramatically over the past decade, reaching 92 million vehicles produced by 2019. This increase has particularly boosted the used car market, emerging as a growing industry thanks to the internet so fashionable methods Need to stay current market trends for determining real value have increased in many applications of machine learning, its use in predicting problems stands out in the way in a remarkable way. This work focuses on the use of machine learning algorithms, in particular linear regression, to predict used car prices and to develop mathematical models using data with specific characteristics. In this period marked by rapid growth, our research is at the forefront of a new approach to car price forecasting. Our solution, using machine learning techniques, aims to provide comprehensive support, significantly saving time and costs associated with car pricing calculations. Forecasting the price of a car is a major one, requiring a great deal of expertise and thorough diligence. We thoroughly examined a variety of factors to make accurate and reliable predictions. We used Linear Regression, a leading machine learning technique, to develop a +model to predict used car prices. This method incorporates multiple independent variables and one dependent variable, so that actual and predicted values can be compared to assess forecast accuracy Our study uses a system with a forced dependent variable on the cost depending on factors such as mileage, year of production, and variety come other characteristics. © 2024 IEEE.

关键词： Data Preprocessing Feature engineering Kaggle Dataset Linear Regression Numpy Vehicle Price Prediction

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Deep learning Inference on Edge: A Preliminary Device Comparison 25th

Deep Learning Inference on Edge: A Preliminary Device Compar...

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25th international conference on Intelligent Data engineering and Automated learning

作者： Gonzalez, Manuel L. Ruiz, Jorge Andres, Lidia Lozada, Randy Skibinsky, Erik S. Fernandez, Jorge Sedano, Javier Garcia-Vico, Angel M. Inst Tecnol Castilla & Leon Burgos Spain Univ Jaen Andalusian Res Inst Data Sci & Computat Intellige Jaen 23071 Spain

ISBN: (纸本)9783031777301;9783031777318

The deployment and inference of Deep learning models are investigated in this article on seven different Edge computing (EC) devices. This study addresses the features, performance and limitations of the NVIDIA Jetson Orin NX and Nano, Google Coral DevBoard and USB, Intel Neural Compute Stick 2, NXP ***8 Plus and Xilinx Zynq UltraScale+ MPSoC ZCU104 on Deep learning inference. Fully Connected, Convolutional and Long Short-Term Memory (LSTM) neural networks are implemented to test these EC devices. The benchmarking focuses on the performance metrics: inference latency, increase in error metric, and power consumption. The results show considerable variability among devices, with the ZCU104 and Jetson Orin achieving the lowest latencies across most models without any increase in the error metric. At the same time, Coral devices exhibit increased latency and error for complex convolutional models. NVIDIA Jetson devices, ZCU104 and Neural Compute Stick 2 are the only devices that support LSTM inference. The study also highlights differences in power consumption, with USB accelerators being the most energy-efficient.

关键词： Edge computing Deep learning Real-Time Inference

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Optimizing Multi-Class Text Classification Models for Imbalanced News Data 15

Optimizing Multi-Class Text Classification Models for Imbala...

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

作者： Anitha, S. Kavi Varshini, E. Haritha Mahalakshmi, N. Jishnu, S. Kongu Engineering College Information Technology Erode India

ISBN: (纸本)9798350370249

In the age of abundant digital journalism, the categorization of news articles has become increasingly crucial for efficient information retrieval and analysis. This article examines different machine learning and deep learning methods used in classifying news articles, with a specific focus on overcoming challenges presented by imbalanced datasets. Conventional techniques like Logistic Regression, Random Forest, and Decision Trees provide interpretability but face difficulties in achieving precision, especially in imbalanced scenarios. Advanced models such as Multi-Class CNN-LSTM, MLP, and RNN exhibit superior performance by effectively capturing both local and global features in news articles. Assessments across various categories showcase the flexibility and reliability of these models, with MLP consistently surpassing others. The research underscores the significance of choosing tailored models and highlights notable progress in news categorization accuracy through advanced methodologies. Ongoing exploration in deep learning and ensemble strategies shows potential for further improvements in tackling the evolving challenges of news categorization within the expansive realm of digital journalism and big data. © 2024 IEEE.

关键词： Adversarial machine learning

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Advances in Optical Metrology: The Impact of Deep learning and Quantum Photonics 6

Advances in Optical Metrology: The Impact of Deep Learning a...

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6th international conference on computing, Electronics and Communications engineering, iCCECE 2023

作者： Bitri, Rea Ali, Maaruf London United Kingdom Universiteti Metropolitan Tiranë Department of Computer Science Tirana Albania

ISBN: (纸本)9798350340921

There have been numerous advances in the fields of engineering, medicine, environment and so forth that involve measurements as a critical part of research and applications. Especially, when it comes to reaching efficient and precision measurements-what comes in handy are optical sensors that provide results beyond just size or location. In recent years, research have primarily relied on providing solutions by using deep learning and novel training algorithms for efficiently determining and eliciting results in the fields that optical metrology is applied. However, there is also analysed optical quantum metrology that is bringing a new finding to light. This paper will analyse the impact of deep learning in quantum metrology and will provide future suggestions on how to improve the results in terms of classical means by using the technologies of quantum photonics metrology to provide actual examples and applications of both technologies in real life. © 2023 IEEE.

关键词： Image processing

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