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检索条件"任意字段=International Conference on Advances in Machine Learning and Systems Engineering"

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Advancements in Climate Modeling and Prediction through machine learning Techniques

Advancements in Climate Modeling and Prediction through Mach...

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2024 IEEE international conference on Recent advances in Science and engineering Technology, ICRASET 2024

作者： Hegde, Ramakrishna Soumyasri, S.M. Shivashankara, S. Sudeep, J. Deepu, G. Natesh, M. Vidyavardaka College of Engineering Affiliated to Visvesvaraya Technological University Mysuru India Vidya Vikas Institute of Engineering and Technology Affiliated to Visvesvaraya Technological University Dept. of Computer Applications Mysuru India Kr Pete Krishna Govt. Engg. College Krishnarajapet India Mysuru India Vidya Vikas Institute of Engineering and Technology Mysuru India Vidyavardaka College of Engineering Mysuru India

ISBN: (纸本)9798350388602

The popularity of machine learning (ML) is increased in the field of weather and climate modelling. Enforcement variety from improved resolution and pre-condition to structure scheme propagation and substitute, and lately even to entire ML climate and climatology predictive models. Linear Structural Equation Models (LSES) have been used, employed and mastered, that for the timed which exceeded 25 years that towards this point have remained dormant. Rather more intensive progress of this domain of knowledge has been observed during the last decade. While proceeding entirely along this line of inquiry, it is most comfortable to use a critical approach in ML applications and the vicinity of weather and climate modelling, at least at the earlier stages. © 2024 IEEE.

关键词： Prediction models

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Speech Emotion Recognition using machine learning

Speech Emotion Recognition using Machine Learning

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2025 international conference on Artificial Intelligence and Data engineering, AIDE 2025

作者： Ajitha, S. Geetanjali, R. Nk, Thushar Thimmaiah Ramaiah Institute of Technology Master of Computer Applications Bangalore India

ISBN: (纸本)9798331527518

Emotion recognition from speech is an emerging field within machine learning, aimed at improving human-computer interaction by enabling systems to understand and respond to human emotions. This paper presents a comprehensive study on Speech Emotion Recognition (SER) using machine learning techniques. We explore various feature extraction methods, including Mel-Frequency Cepstral Coefficients (MFCCs) and chroma features, to capture the emotional content from speech signals. The extracted features are utilized to train several machine learning models, including Support Vector machines (SVM), Random Forests, and Convolutional Neural Networks (CNNs). Our approach is evaluated on publicly available datasets, and the performance is assessed using metrics such as accuracy, precision, recall, and F1-score. The experimental results demonstrate that our proposed models achieve significant improvements in emotion classification accuracy compared to existing methods. Additionally, we discuss the challenges and potential applications of SER in real-world scenarios, such as customer service, mental health monitoring, and virtual assistants. The findings of this study contribute to the advancement of robust and efficient SER systems, paving the way for more empathetic and intuitive human-computer interactions.. © 2025 IEEE.

关键词： Feature extraction

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Soil Monitoring and Crop Recommendation System via IoT and machine learning 11

Soil Monitoring and Crop Recommendation System via IoT and M...

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11th IEEE international conference on advances in Computing and Communications, ICACC 2024

作者： Bennila Thangammal, C. Poorna Saai, M. Muhammad Taufeeq Umar, N. Nithissh, S.S. Murugesh, K. Tharun Kumar, S. R. M. D. Engineering College Electronics and Communication Engineering Kavaraipettai India R. M. D. Engineering College Artificial Intelligence and Machine Learning Engineering Kavaraipettai India

ISBN: (纸本)9798350379136

Traditional practices in agriculture lack the proper guidance in regard to crop production and yields, making the processes inefficient with Poor use of resources. To address the above challenges, we proposed an IoT and machine learning-based system targeting on the improvement of precision farming. The technology incorporated into our system is a microcontroller, numerous soil sensors and a GSM for transmitting the data. The main focus of our study is accurate crop recommendations that depend on which crops are suitable for the farmer and his environment. This is done via, evaluating some of the key factors of the environment in terms of Nitrogen, Phosphorous, and potassium Content in the soil, its Ph Level, the temperature, humidity and TDS. The way it works is that an image or a video of the soil is taken while, at the same time, it monitors the real-time data of the soil parameters using the sensors. Then it sends this data to a GSM module with IoT technology which connects to a model website on the cloud. The information is then processed by enhanced machine learning formulas in order to determine the fertility level of the soils and the type of crops to be grown on the land. The processed data and recommendations are sent back to the device with enabled user interface to see the analyzed results on a display. Here our use of IoT with machine learning enables the system give accurate and recommendable crop data, making the most of the resources and maximizing crop production. It bypasses the shortcomings of conventional ways of agricultural methods offering a scalable and efficient solution for modern agriculture. © 2024 IEEE.

关键词： Smart agriculture

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Intelligent Decision-Making in Cybersecurity Using machine learning 12th

Intelligent Decision-Making in Cybersecurity Using Machine L...

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12th international conference on Frontiers of Intelligent Computing: Theory and Applications, FICTA 2024

作者： Sakhare, Nitin N. Maruthi Shankar, B. Kalnawat, Aarti Sharma, Abha Rege, P.R. Wagh, Kapil Adhar Department of Computer Engineering BRACT’s Vishwakarma Institute of Information Technology Maharashtra Pune India Department of Electronics and Communication Engineering Sri Krishna College of Engineering and Technology Tamil Nadu Coimbatore India Pune India Electronics and Communication Engineering Arya College of Engineering Rajasthan Jaipur India Department of Computer Engineering Vishwakarma Institute of Information Technology Maharashtra Pune India Nutan Maharashtra Institute of Engineering and Technology Maharashtra Pune India

ISBN: (纸本)9789819601462

In the constantly changing field of cybersecurity, the need for advanced decision-making tools is more crucial than ever. This study explores machine learning in order to introduce an innovative method for strengthening cybersecurity protocols. The methodology involves combining Random Forests (RF) and Support Vector machines (SVM) using the AdaBoost algorithm in an ensemble framework. The ensemble model is intricately crafted to address the complexities of intrusion detection, utilizing the combined capabilities of Random Forest’s data handling and Support Vector machine’s pattern discrimination. AdaBoost enhances the ensemble’s predictive abilities by iteratively adjusting weights to focus more on misclassified instances. This adaptive learning process enables the model to constantly develop and adjust to new cyber threats. Validation against benchmark datasets demonstrates an impressive accuracy rate of 97.78%, highlighting the strength of the ensemble methodology. The True Positive Rate (TPR) of 98.33% demonstrates the ensemble’s effectiveness in accurately detecting and responding to potential cyber threats. The results demonstrate the superior performance of the proposed ensemble method and highlight its ability to achieve a balance between accuracy and adaptability. This research significantly advances intelligent decision-making in cybersecurity by introducing a potent ensemble approach that smoothly integrates various machine learning algorithms. The method’s proven accuracy and sensitivity make it a powerful tool in combating the increasing complexity of cyber threats. The combination of RF, SVM, and AdaBoost sets a standard for future research in strengthening digital defenses and ensuring the resilience of critical information systems. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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Plant Disease Detection using machine learning 2

Plant Disease Detection using Machine Learning

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2nd international conference on advances in Computation, Communication and Information Technology, ICAICCIT 2024

作者： Batra, Ashmit Kang, Prabaldeep Singh Setia, Sambhav Aparna Menka Dagar, Vivek Sharma, Anshu Kumar, Kapila School of Engineering and Technology Manav Rachna International School of Research and Studies Haryana Faridabad India

ISBN: (纸本)9798331541217

This research explores the use of machine learning approaches for accurate identification and detection of major diseases in pulse crops, targeting improvements in early diagnosis and management of infections such as downy mildew, Ascochyta blight, rust, powdery mildew, and anthracnose. These diseases trigger diverse signalling pathways. Specific genes and proteins, are upregulated in response to pathogen invasion, enabling plants to mount targeted defence responses. Understanding these mechanisms highlights opportunities for enhancing disease resistance through breeding and genetic research in legume crops. This paper outlines a procedure for identification of diseases in pulse crops using modern image processing technologies and machine learning techniques. The objective is to build a system that can recognize the common ailments of pulses which would otherwise lead to a huge loss in case of the weeds not being controlled. The system uses a data set of diseased and healthy plants and applies CNN's to classify and predict disease. The findings achieved very encouraging Level of accuracy thus suggesting further work could extend to practical use by farmers in assessing the health of the crops more conveniently. © 2024 IEEE.

关键词： Adversarial machine learning

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Improve the Quality of machine Translation in Low-Resource Language Pairs 3rd

Improve the Quality of Machine Translation in Low-Resourc...

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3rd international conference on advances in Information and Communication Technology, ICTA 2024

作者： Nguyen-Tien, Ha Nguyen-Ngoc, Phuc Nguyen-Van, Vinh Kim, Thai Dinh Hung Vuong University Phutho Viet Nam University of Engineering and Technology - VNU Hanoi Viet Nam International School Vietnam National University Hanoi100000 Viet Nam

ISBN: (纸本)9783031809422

machine translation is a hard problem in Natural Language Processing (NLP). Today, based on deep learning, the quality of machine translation systems is increasing strongly in rich-resource language pairs, but it is limited in low-resource language pairs, such as the Vietnamese-Laos language pair. In this work, we propose a method that depends on augmenting data and using a bilingual dictionary to improve the quality of machine translation for low-resource language pairs. Applying it to the Vietnamese-Laos language pair, it improved 14.0 BLEU scores for translating from Laos to Vietnamese and 3.5 BLEU scores for translating from Vietnamese to Laos. In addition, we built a good quality Vietnamese-Laos bilingual corpus that includes 182.208 sentence pairs to share with the community. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： machine translation

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Maize Crop Yield Prediction Using machine learning Regression Approach 5th

Maize Crop Yield Prediction Using Machine Learning Regressio...

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5th international conference on advances in Distributed Computing and machine learning, ICADCML 2024

作者： Bedane, Tarikwa Tesfa Kebede, Kidistie Bizuneh Mohapatra, Sudhir Kumar Das, Tapan Kumar Tripathy, Asis Kumar Department of Software Engineering Addis Ababa Science and Technology University Addis Ababa Ethiopia Agricultural Transformation Institute Addis Ababa Ethiopia Faculty of Emerging Technologies Sri Sri University Cuttack India School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore India

ISBN: (纸本)9789819735228

Given maize’s significant role as a staple crop, it becomes imperative to carry out precise crop yield prediction to ensure food security. This research employs machine learning algorithms to analyze historical data related to maize crop yields, with the aim of developing a predictive model. The outcome of this study can serve as a valuable tool for pre-harvest planning and measuring crop production per unit of land area. The dataset utilized in this research spans from 2010 to 2021 and was sourced from Ethiopia’s Central Statistical Agency. Three machine learning regression algorithms—Logistic Regression, XGBoost, and Random Forest—were employed to design the predictive model. After data preparation and preprocessing, a total of 436,358 records were used for model development. The models were further trained with reduced features using principal component analysis (PCA). The experimental results demonstrate that the Random Forest algorithm, trained with a reduced number of features through PCA, yielded superior performance, with a lower error rate and a high R2 score of 0.98. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： machine learning

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A Hybrid Deep learning Model for Optimizing Electric Vehicle Battery and Navigation systems 2

A Hybrid Deep Learning Model for Optimizing Electric Vehicle...

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2nd international conference on machine learning and Autonomous systems, ICMLAS 2025

作者： Arul Jose, R. Ebby Darney, P. Santhana Krishnan, R. Relin Francis Raj, J. Jansi Rani, D. Sundaravadivel, P. Rajarajeswari College of Engineering Department of Electrical and Electronics Engineering Bengaluru India SCAD College of Engineering and Technology Department of Electronics and Communication Engineering Cheranmahadevi India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Electronics and Communication Engineering Chennai India Velammal College of Engineering and Technology Department of Computer Science and Engineering Madurai India Saveetha Engineering College Department of Artificial Intelligence and Machine Learning Chennai India

ISBN: (纸本)9798331505745

This paper proposes a deep learning-based optimization framework for Electic Vehicle that leverages LSTM, CNN, and DRL to enhance battery management, autonomous navigation, and energy efficiency. The LSTM model predicts the State of Charge (SoC) and State of Health (SoH) of the EV battery, utilizing temporal patterns in sensor data to improve predictive maintenance. The CNN-based model focuses on real-time obstacle detection and fault recognition in autonomous driving systems, processing visual data from onboard cameras, LiDAR, and radar sensors. Finally, the DRL model optimizes energy consumption and route planning by training an agent to navigate traffic and road conditions efficiently while minimizing power usage. Data for the system is collected from various sensors embedded in the vehicle, including battery data, traffic information, GPS, and driver behavior. The data is preprocessed and augmented to ensure the models are robust and generalizable. Training involves GPU-accelerated computing platforms to enhance model efficiency. The performance of the models is evaluated through metrics such as R2 score, accuracy, and energy efficiency improvements, with results showing a 20-30% enhancement in energy consumption and battery life. The proposed system has practical applications in battery management, autonomous driving, and real-time energy optimization, contributing to the advancement of intelligent EV technologies. The work presents a step toward integrating deep learning techniques into EVs for improved performance and operational efficiency. © 2025 IEEE.

关键词： State of charge

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AI Meets Cyber Defense: Enhancing Network Security with GAN-Driven NIDS 11

AI Meets Cyber Defense: Enhancing Network Security with GAN-...

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11th IEEE international conference on advances in Computing and Communications, ICACC 2024

作者： Sarika, S.P. Paul, Arya Kumar, Ajay George, Anju Adi Shankara Institute of Engineering & Technology Electronics & Communication Engineering Ernakulam India

ISBN: (纸本)9798350379136

The field of cybersecurity has witnessed a growth in the breadth and intricacy of cyber threats due to the advancement of communication technology, which calls for the implementation of strong network intrusion detection systems (NIDS). Regrettably, a wide range of potential hazards might put computer users at risk when using the Internet. Users stand to lose a great deal, including control over machines, private information, and potentially even identity theft. The ongoing problem of inconsistent and changing threat data persists despite notable improvements in NIDS models. As more and more research has realized the need for improved detection skills, artificial intelligence (AI) has become a viable answer. In order to meet the challenges presented by emerging cyber threats, this study presents a thorough investigation into the integration of AI, specifically generative adversarial networks (GANs), into NIDS. GANs are a machine learning breakthrough that provides a fresh method for creating new data instances that closely mimic real data. This skill is especially useful in the context of network intrusion detection systems (NIDS), where identifying anomalies necessitates a sophisticated comprehension of intricate and dynamic threat patterns. Our study expands on earlier research in the field that mostly concentrated on deep learning methods like autoencoders, convolutional neural networks (CNN), and long short-term neural networks (LSTM), as well as conventional machine learning models like decision trees (DT) and support vector machines (SVM). Although these methods have shown potential, there hasn't been much of a practical use for them in actual NIDS systems. A promising approach to improving NIDS performance is provided by the suggested NIDS makes use of GANs, especially when it comes to anomaly detection. Through the creation of artificial data instances that closely resemble real-world threats, GANs can enhance the resilience and flexibility of NIDS in dynami

关键词： Network intrusion

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Intelligent E-Commerce Chatbot for Personalized Product Recommendations Using Text Mining and machine learning 2

Intelligent E-Commerce Chatbot for Personalized Product Reco...

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2nd international conference on advances in Computation, Communication and Information Technology, ICAICCIT 2024

作者： Minu, M.S. Adithya Kavya SreeRanjini Department of Computer Science and Engineering SRM Institute of Science and Technology Ramapuram Chennai India Department of Computer Science and Engineering with Specialization in Big Data Analytics SRM Institute of Science and Technology Ramapuram Chennai India

ISBN: (纸本)9798331541217

Innovative technology solutions have been developed in response to the growing need for effective and customized client contact on e-commerce platforms. This work introduces an intelligent chatbot system that uses machine learning and text mining techniques to improve user satisfaction and answer customer concerns. Advanced Natural Language Processing (NLP) models have been trained on large datasets, allowing for precise user intent interpretation and pertinent product recommendations. A strong recommendation engine is included into the system architecture to aid in decision-making, ultimately enhancing the customer experience by providing individualized, real-time help. The study also highlights the significance of data privacy by using privacy-preserving techniques to safeguard user data. The system's scalability and performance benefits are highlighted through thorough comparisons with current models. By showcasing the useful uses of machine learning and text mining in enhancing customer contact and service delivery, this study makes a substantial contribution to the development of AI-driven solutions in e-commerce. © 2024 IEEE.

关键词： Big Data Chatbot Cost Data Mining e-Commerce Hadoop machine learning Product Purchasing Recommendation

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