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检索条件"任意字段=1st International Conference on Machine Learning Algorithms, ICMLA 2024"

共 3724 条记录，以下是11-20 订阅

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Financial time series forecasting using machine learning: An empirical analysis 1st

Financial time series forecasting using machine learning: An...

引用

1st international conference on Applications of AI in 5G and IOT, ICAAI5GI 2024

作者： Gupta, Gunjan Gupta, Zatin Sharmila Bindal, Amit Kumar Nand Mishra, Achyuta Gupta, Urvashi Department of Electrical Electronics and Computer Engineering Cape Peninsula University of Technology Cape Town South Africa Sharda University Uttar Pradesh Greater Noida India Government Girls Polytechnic Uttar Pradesh Bareilly India Ambala Haryana Mullana India Supaul College of Engineering Bihar Supaul India KIET Group of Institutions Uttar Pradesh Ghaziabad India

ISBN: (纸本)9781032874036

Economic research focuses on estimating currency exchange rates, especially after the financial crisis. Accurate predictions help investors avoid financial bubbles and crashes. Advancements in machine learning (ML) have led to the development of more complex models, such as the ARIMA model. This study identifies a financial time series forecasting model using a support vector machine (SVM) and investigates whether deep learning-based algorithms like Long Short-Term Memory (LstM) outperform traditional approaches. Results show LstM outperforms traditional models. © 2025 the Author(s).

关键词： Adversarial machine learning

来源：评论

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Investigating features for lung cancer classification using machine learning 1st

Investigating features for lung cancer classification using ...

引用

1st international conference on Applications of AI in 5G and IOT, ICAAI5GI 2024

作者： Tyagi, Poornima Singh, Pragya Ahmed, Ibrar Rajput, Neetu Kumari Srivastava, Anamika Sisaudia, Vaishali Sharma, Himanshu Noida Institute of Engineering and Technology Greater Noida India

ISBN: (纸本)9781032874036

Lung cancer is global health concern, necessitating accurate and efficient methods for patient classification. This paper presents a comparative study of various classification methods applied to lung cancer patients. We explore the effectiveness of machine learning and statistical approaches in accurately categorizing patients. We evaluate the impact of classification methods on treatment decisions and discuss future directions for lung cancer research and machine learning applications. In this experiment, we used k-nearest Neighbourhood (KNN), Random Forest, Support Vector machines (SVM) and AdaBoost algorithms. We used the Lung Cancer dataset from Kaggle containing fifteen features for 309 patients. Our experimental results showed that, the accuracy of random forest algorithm is highest 0.95 as compared to other methods. © 2025 the Author(s).

关键词： Lung cancer

来源：评论

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Predicting Diabetes Risk: A Comparative Analysis of machine learning algorithms 1

Predicting Diabetes Risk: A Comparative Analysis of Machine ...

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1st international conference on Advances in Computer Science, Electrical, Electronics, and Communication Technologies, CE2CT 2025

作者： Thakur, Hardeo Kumar Sahu, Dinesh Prasad Chaturvedi, Rajnish Kumar Jha, Govind Kumar Kumari, Divya School of Computer Science Engineering and Technology Bennett University Greater Noida India Government Engineering College Department of CSE Bihar Munger India USICT GGSIPU Delhi India

ISBN: (纸本)9798331518578

Nowadays, diabetes mellitus is a worldwide health concern that is becoming more common. Therefore, for better early detection techniques (machine learning) is need of hour. Six machine learning algorithms are applied in this article to forecast the likelihood of diabetes in three stages: prediabetes, diabetes, and no diabetes. Three metrics i.e., recall, F1 score and precision are applied to evaluate the algorithms performance. Additionally, we have used the Synthetic Minority Oversampling Technique (SMOTE) for improving class imbalance. The results show that different algorithms and stages of diabetes function differently, which emphasizes the necessity of choosing algorithms according to the demands of certain applications. © 2025 IEEE.

关键词： Contrastive learning

来源：评论

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machine learning based application for predicting and preventing the injury of a sportsperson 1st

Machine learning based application for predicting and preven...

引用

1st international conference on Applications of AI in 5G and IOT, ICAAI5GI 2024

作者： Malik, Rakshit Batra, Parth Sarowa, Aryan Mala, Shuchi Department of CSE Amity University Uttar Pradesh Noida India

ISBN: (纸本)9781032874036

In sports, injuries pose significant challenges, disrupting athletes’ careers and causing physical, emotional, and financial strain on teams and organizations. This project aims to address this issue by developing a machine learning-based application to predict and prevent injuries in athletes. The primary goal is to create a system using historical data, realtime monitoring, and advanced algorithms to predict injury risks. By analyzing factors like physical condition, training routines, and environmental variables, the application will generate risk scores and identify high-risk individuals while providing insights into injury causes. This will help coaches, trainers, and athletes make informed decisions on training, recovery, and lifestyle adjustments to reduce injury rates and enhance performance. Additionally, integrating wearable sensors for real-time feedback on biomechanics, heart rate, and muscle fatigue will enable proactive interventions. This project represents a significant advancement in sports science, promoting safer and more effective training and competition. © 2025 the Author(s).

关键词： Adversarial machine learning

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IoT and machine learning based early landslide detection and warning system 1st

IoT and machine learning based early landslide detection and...

引用

1st international conference on Applications of AI in 5G and IOT, ICAAI5GI 2024

作者： Shukla, Pavan Kumar Ranjan, Aaditya Kumar, Aalok Addy, Utsav Department of ECE Noida Institute of Engineering and Technology UP Greater Noida India

ISBN: (纸本)9781032874036

Landslides pose significant threats to hilly regions, particularly in India, where the rugged terrain and monsoon seasons exacerbate the risk of slope instability. This paper presents an innovative approach to early landslide detection and warning systems using IoT (Internet of Things) devices and machine learning algorithms. The integration of sensors measuring rainfall intensity, temperature, humidity, and soil moisture with an Arduino Uno microcontroller forms the hardware backbone of the system. The collected sensor data are then processed using Panda’s library in Python, and a random forest classifier is employed for landslide prediction. The system triggers a warning signal, indicated bya buzzer, when specific thresholds of environmental parameters are exceeded, thereby providing early alerts to mitigate the impact of landslides. This research contributes to the development of proactive measures for landslide risk management, enhancing the safety and resilience of hilly regions. Furthermore, the proposed system offers a cost-effective and scalable solution for landslide detection, leveraging readily available IoT components and open-source software libraries. By harnessing the power of machine learning algorithms, the system can continuously adapt and improve its predictive capabilities based on real-time environmental data. © 2025 the Author(s).

关键词： Soil moisture

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Exploring machine learning algorithms for Sentiment Analysis in the Sphere of Cyberbullying Detection 1

Exploring Machine Learning Algorithms for Sentiment Analysis...

引用

1st international conference on Advances in Computer Science, Electrical, Electronics, and Communication Technologies, CE2CT 2025

作者： Bisht, Lalita Chaudhary, Kamika MBGPG College Haldwani Kumaun University Department of Computer Science Nainital India

ISBN: (纸本)9798331518578

The rise in cybercrimes targeting children is leading to devastating consequences like harmful behaviors and even suicide. While social media platforms offer reactive solutions like post deletion and blocking, a more proactive approach is needed. To combat cyber child abuse effectively we need technology-driven solutions that focus on detection and alerts to identify predators or protect victims. Various strategies have been proposed, including machine learning techniques that leverage sentiment analysis to detect patterns in the bully's language. This study explores the potential of machine learning to combat cyber child abuse by focusing on detection and alerts. We investigated the effectiveness of supervised, unsupervised, and ensemble learning algorithms for sentiment analysis in identifying abusive language. Analyzing a social media dataset for bullying detection, the research reveals that Support Vector machines achieved the highest accuracy (83.26%) among supervised algorithms, with K-Nearest Neighbor (82.97%) and Random Forest Classifier (87.74%) performing best in the unsupervised and ensemble categories, respectively. The paper also addresses limitations and proposes future research directions. © 2025 IEEE.

关键词： Adversarial machine learning

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Cloud Based IDS-Anomaly Detection Using machine learning 1

Cloud Based IDS-Anomaly Detection Using Machine Learning

引用

1st international conference on AIML-Applications for Engineering and Technology, ICAET 2025

作者： Rachani, Raj Mahendrakumar Ravish, Roopa P E S University Department of Computer Science Bangalore India

ISBN: (纸本)9798350355611

As organizations increasingly rely on cloud computing for their critical operations, securing cloud infrastructures against cyber threats has become paramount. This paper presents a robust and scalable Intrusion Detection System (IDS) designed for cloud environments, leveraging machine learning techniques for efficient anomaly detection. The proposed system aims to enhance the detection capabilities of traditional IDS by harnessing the power of diverse machine learning models within a cloud-based architecture. The machine learning approach combines multiple anomaly detection algorithms, each contributing its unique perspective, to form a comprehensive and adaptive defense mechanism. Leveraging cloud resources enables the system to dynamically scale its computational power based on demand, ensuring real-time monitoring and response to emerging threats. The IDS utilizes a combination of feature engineering and extraction methods to process large-scale data generated by cloud applications, allowing for effective anomaly detection without compromising system performance. © 2025 IEEE.

关键词： Adversarial machine learning

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Optimizing of 6G Network Performance Through machine learning by Allocating Resources 1

Optimizing of 6G Network Performance Through Machine Learnin...

引用

1st international conference on Advances in Computer Science, Electrical, Electronics, and Communication Technologies, CE2CT 2025

作者： Agarwal, Ankita Thulasiram, Ramachandran Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Faculty of Engineering and Technology Department of Mechanical Engineering Ramnagar District Karnataka India

ISBN: (纸本)9798331518578

The impediments with 6G network construction have encouraged the activity of machine learning Resource Allocation algorithms (MLRA). In terms of flexibility and ability utilisation., MLRA outperforms more normal algorithms. Using data on how the network is functioning, the algorithms can resolve how to deliver resources most efficiently. To confirm that properties are delivered accurately and capably, MLRA will be decisive as 6G networks are employed to a greater extent. Quickly reallocating unaccustomed resources to more creative consumers or apps is entity the algorithms are fit achievement. The expected GB speeds and a 1000x increase in illiteracy from 6G networks are intense. Smart talent allocation algorithms that can exactly and expeditiously readjust to changeful consumer demands and behaviours are essential for this. The change to 6G networks and the facilities they supply will not be likely without MLRA. © 2025 IEEE.

关键词： Queueing networks

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Human Prakruti Prediction Using machine learning Techniques 1

Human Prakruti Prediction Using Machine Learning Techniques

引用

1st international conference on AIML-Applications for Engineering and Technology, ICAET 2025

作者： Ainapure, Bharati Pise, Reshma Bhutad, Sonali Ainapure, Ankita Murlidhar Chaudhari, Vivek Suryawanshi, Siddhesh Vishwakarma University Department of Computer Engineering Maharashtra Pune India Shah and Anchor Kutchhi Engineering College Department of Computer Engineering Maharashtra Mumbai India Hadapsar Pune India

ISBN: (纸本)9798350355611

This paper presents the results of various machine learning methods, including Multimodal Naïve base, Support Vector machine, Decision Tree, K-Nearest Neighbor,, and Random Forest in predicting human body constituencies. Ayurveda-dosha investigations have long been used;however the quantitative dependability of these diagnostic approaches remains unexplored. Careful and thorough analysis can lead to more effective treatments for predicting body constituencies. The results show that the random forest algorithm, with validation, achieves superior accurateness, recall, precision and F-score (95.2 %). The experimental findings demonstrate that the enhanced model, i.e. random forest classifier based on ensemble learning methods, significantly outperforms traditional approaches. These results suggest that advances in the proposed algorithms could lead to a promising future for Ayurveda treatments using machine learning. © 2025 IEEE.

关键词： Adversarial machine learning

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Enhancing Malicious URL Detection with Advanced machine learning Techniques 1

Enhancing Malicious URL Detection with Advanced Machine Lear...

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1st international conference on Advances in Computer Science, Electrical, Electronics, and Communication Technologies, CE2CT 2025

作者： Alavala, Hemanth Reddy Singh, Shambhavi Joshi, Priyam Basavaraju, Sagar Amrita School of Engineering Amrita Vishwa Vidyapeetham Department of Electronics and Communication Engineering Bengaluru India

ISBN: (纸本)9798331518578

Malicious URLs pose a significant threat to online security. The can steal our personal information and can even hack out system. Refraining ourselves from using such malicious websites is the need of the hour. This paper presents a comprehensive evaluation of various machine learning algorithms which are used to effectively detect and mitigate these threats. This research explores the performance of traditional machine learning techniques including Random Forests, Decision Trees, Logistic Regression, Support Vector machines, as well as advanced techniques like XGBoost, LightGBM, and CatBoost while the previous works focused on only traditional classification techniques. Additionally, this work investigates the potential of deep learning models, including LstM and CNN, to capture intricate patterns within URL sequences. machine learning algorithms are evaluated using the evaluations metrics including Accuracy, Recall, Precision and F1 score. Experimental Results and analysis have shown that advanced models outperform traditional classification techniques. These results valuable insights into the most effective approaches for safeguarding online users from malicious attacks. © 2025 IEEE.

关键词： Phishing

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