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检索条件"任意字段=2017 International Conference on Machine Learning and Soft Computing, ICMLSC 2017"

共 2746 条记录，以下是61-70 订阅

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A New Computational Approach to the Levenberg-Marquardt learning Algorithm 21st

A New Computational Approach to the Levenberg-Marquardt Lear...

引用

21st international conference on Artificial Intelligence and soft computing (ICAISC)

作者： Bilski, Jaroslaw Kowalczyk, Barosz Smolag, Jacek Czestochowa Tech Univ Inst Computat Intelligence Czestochowa Poland

ISBN: (纸本)9783031234910;9783031234927

A new parallel computational approach to the Levenberg-Marquardt learning algorithm is presented. The proposed solution is based on the AVX instructions to effectively reduce the high computational load of this algorithm. Detailed parallel neural network computations are explicitly discussed. Additionally obtained acceleration is shown based on a few test problems.

关键词： Neural network learning algorithm Levenberg-marquardt learning algorithm Vector computations Approximation Classification

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Network Intrusion Detection Using Weighted Voting Ensemble Deep learning Model 11

Network Intrusion Detection Using Weighted Voting Ensemble D...

引用

11th international conference on Electrical, Electronic and computing Engineering, IcETRAN 2024

作者： Milosevic, Marija Ciric, Vladimir Milentijevic, Ivan University of Nis Faculty of Electronic Engineering Nis Serbia

ISBN: (纸本)9798350386998

System and network security have become one of the main considerations for the system or network design, implementation and maintenance. The increased volume of traffic and the ever-changing form of internet communications has led to a steep rise in network attacks, hence the need to protect both sides of the communication is essential. Over the last couple of years, an upsurge in the use of machine learning and deep learning techniques in detecting network attacks can be noticed. Alongside with individual machine and deep learning techniques, various ensemble techniques have been implemented in order to combine multiple models working on the same problem. However, ensemble techniques mostly focus on machine learning models, disregarding the fact that deep learning techniques have been proven to be efficient when handling large amounts of data. In this paper, a network intrusion detection system is implemented using a weighted voting ensemble deep learning model. Fifteen various Deep Neural Network models have been trained and evaluated on the imbalanced multiclass CICIDS-2017 dataset. Weighted voting system has been implemented for combining decisions of various models in two ways. The evaluation results for multiple combinations are given. The proposed system has the ability to use heterogeneous models for the decision. © 2024 IEEE.

关键词： Network intrusion

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DDoS Attack Detection on Cloud computing Services using Algorithms of machine learning: Survey 7

DDoS Attack Detection on Cloud Computing Services using Algo...

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7th international conference on computing Methodologies and Communication, ICCMC 2023

作者： Sathvika, Chilla Satwika, Vuyyuru Sruthi, Yarrapothu Geethika, Maddali Bulla, Suneetha Swathi, K. Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram India

ISBN: (纸本)9781665464086

Nowadays cloud computing services have become the most popular internet-based computing and many organizations use their services. Due to this, many cyberattacks are happening in the cloud. One of those attacks is the Distributed-Denial-Of-Service (DDoS) attack. It floods unreal traffic, hence troubles the availability of the resources. This article is about DDoS attacks and detection of DDoS attacks using machine learning. There are many famous machine learning algorithms such as naïve bayes, random forest, support vector machines etc. These machine learning algorithms can be used to detect the DDoS attacks on cloud. There are several datasets available for the researchers to test their proposed models which include NSL-KDD, ICDX, CIDDS-001, CICIDS 2017 etc. This paper presents a detailed study on different machine learning based techniques proposed by various authors to detect the DDoS attack in the cloud environment. A brief explanation has been provided on the available datasets and further discussed about the general methodology. © 2023 IEEE.

关键词： Denial-of-service attack

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A machine-learning Approach to Classify Depressive Posts from Reddit Social Media

A Machine-Learning Approach to Classify Depressive Posts fro...

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2023 international conference on Next-Generation computing, IoT and machine learning, NCIM 2023

作者： Quader, Adib Wahid Jamil, Md Shafayet Uddin, Mohammad Irfan Patwary, Muhammed J. A. International Islamic University Department of Computer Science & Engineering Chittagong Bangladesh

ISBN: (纸本)9798350316001

Depression leading to suicide has become a major concern in our society. It's high time to investigate the reason behind it. As numerous people are expressing their concerns regarding depression on Reddit, such post can be taken into consideration in order to find the reason behind it. There has been a lot of research work in this field but most of it is done using traditional machine learning approach with NLP techniques. Moreover, recently there's has not been any significant research work regarding depression. In this paper, we found the indicators that could indicate how relevant Reddit users feel about depression using machine learning, ensemble learning, and deep learning techniques. Moreover, we pinpointed the terms that appear more frequently in narratives of depression. To strengthen the research work, we took the dataset over the period from 2017 to 2021. Furthermore, we demonstrated the effectiveness of LSTM & BERT on recent dataset. © 2023 IEEE.

关键词： learning systems

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Sparse-learning-based High-order Dynamic Functional Connectivity Networks for Brain Disease Classification 23

Sparse-learning-based High-order Dynamic Functional Connecti...

引用

7th international conference on machine learning and soft computing, icmlsc 2023

作者： Wang, Jianhui Jie, Biao Zhang, Xingyu Li, Wen Wu, Zhaoxiang Yang, Yang School of Computer and Information Anhui Normal University Anhui Wuhu China

ISBN: (纸本)9781450398633

Dynamic functional connectivity network (DFCN) derived from resting-state functional magnetic resonance imaging (rs-fMRI), which characterizes the dynamic interaction between brain regions, has been applied to classification of brain diseases. However, existing studies usually focus on dynamic changes of low-order (i.e., pairwise) correlation of brain regions, thus neglecting their high-order dynamic information that could be important for brain disease diagnosis. Therefore, in this paper, we first propose a novel sparse learning based high-order DFCNs construction method, and then build a novel learning framework to extract high-level and high-order temporal features from the constructed high-order DFCNs for brain disease classification. The experimental results on 174 subjects from from the Alzheimer's Disease Neuroimaging Initiative (ADNI) demonstrate the effectiveness of our proposed method in comparison with state-of-the-art methods. © 2023 ACM.

关键词： Magnetic resonance imaging

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Detection of Abductors Using machine learning Algorithms 1

引用

6th international conference on soft computing and Signal Processing, ICSCSP 2023

作者： Baba, Mohammad Asif Varun, Thupakula Sai Shalivahana, Madasu Shaik, Subhani Iyengar, N. Ch. Sriman Narayana Telangana Hyderabad India

ISBN: (数字)9789819986286

ISBN: (纸本)9789819986279

The objective of this project is to help the police personnel and higher authorities to track down missing people quickly. The usual process to track down a missing person is using different types of investigation methods which include case diaries, spot visit, collection of evidences, searches and seizures, witness examinations, etc., that requires lot time and experience in that field. Most of the times, investigation methods work pretty well, but it is time consuming and can be unsuccessful if the person has been shifted to different location. This project proposes a system that would help the police and the public by speeding up the process of searching. We are using image processing and machine learning algorithms along with Postgres SQL database to insert, update, and delete the information regarding the missing person and help the authorities to find the abductor as soon as possible (Sisodia et al., Evaluation of machine learning models for employee churn prediction, 2017). The AI is used to push the image that is closest to the victim’s facial features. There are various NGO’s and Government initiatives to help with it. This paper tries to implement a new way to help the police personnel and higher authorities. We connect this system to the public cameras where the public cameras keep on recording all the details in its environment, and when it matches an image with the image present in the database, then it captures the image and store the location into the database. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： machine learning

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A Long-Short Term Memory Model-based approach for smart intrusion detection systems 15

A Long-Short Term Memory Model-based approach for smart intr...

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

作者： Reddy, Dasari Deepthi Himabindu, G. School of Technology Dept. of Cse Telangana Hyderabad India

ISBN: (纸本)9798350370249

Academics interested in developing intrusion detection systems (IDS) gathered the CSE-CIC IDS datasets in 2017 and 2018. A recent study utilizing these datasets revealed the need to address their unbalanced distribution. This paper investigates imbalance improvement and provides a classification of machine learning and deep learning techniques developed using these datasets. This paper will develop a working model to help businesses implement and maintain an intrusion detection system with fewer resources. This model will standardize and optimize this goal by comparing four models with two datasets. After that, a novel inter-dataset assessment mechanism estimates each method's generalization strength. To test each model's generalizability evaluations, use the latest datasets to ensure reliability. Experimental validation shows that the long- and short-term memory-based intrusion detection model's accuracy and false alarm rate are significantly higher than those of other standard models. However, each model had high classification scores on each dataset. The study's conclusions yielded these results. © 2024 IEEE.

关键词： Adversarial machine learning

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Deep learning-Enabled Prediction of Daily Solar Irradiance from Simulated Climate Data 23

Deep Learning-Enabled Prediction of Daily Solar Irradiance f...

引用

7th international conference on machine learning and soft computing, icmlsc 2023

作者： Gerges, Firas Boufadel, Michel C. Bou-Zeid, Elie Nassif, Hani Wang, Jason T. L. New Jersey Institute of Technology United States Princeton University United States Rutgers University - New Brunswick United States

ISBN: (纸本)9781450398633

Solar Irradiance depicts the light energy produced by the Sun that hits the Earth. This energy is important for renewable energy generation and is intrinsically fluctuating. Forecasting solar irradiance is crucial for efficient solar energy generation and management. Work in the literature focused on the short-term prediction of solar irradiance, using meteorological data to forecast the irradiance for the next hours, days, or weeks. Facing climate change and the continuous increase of greenhouse gas emissions, particularly from the use of fossil fuels, the reliance on renewable energy sources, such as solar energy, is expanding. Consequently, governments and practitioners are calling for efficient long-term energy generation plans, which could enable 100% renewable-based electricity systems to match energy demand. In this paper, we aim to perform the long-term prediction of solar irradiance, by leveraging the downscaled climate simulations of Global Circulation Models (GCMs). We propose a novel Bayesian deep learning framework, named DeepSI (denoting Deep Solar Irradiance), that employs bidirectional long short-term memory autoencoders, prefixed to a transformer, with an uncertainty quantification component based on the Monte-Carlo dropout sampling technique. We use DeepSI to predict daily solar irradiance for three different locations within the United States. These locations include the Solar Star power station in California, Medford in New Jersey, and Farmers Branch in Texas. Experimental results showcase the suitability of DeepSI for predicting daily solar irradiance from the simulated climate data. We further use DeepSI with future climate simulations to produce long-term projections of daily solar irradiance, up to year 2099. © 2023 ACM.

关键词： Solar energy

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Optimizing Crime Prevention Strategies Using machine learning Models 1

Optimizing Crime Prevention Strategies Using Machine Learnin...

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1st international conference for Women in computing, InCoWoCo 2024

作者： Ajitha, V. Sherly, S. Irin Venitha, E. Senthilkumar, M. Vishalakshi, N. Bhavyadevi Prathipa, R. Nalla Malla Reddy Engineering College Department of CSE Hyderabad India Department of CSE Chennai India Rajalakshmi Institute of Technology Department of CSE Chennai India Kongunadu College of Engineering and Technology Department of AI & DS Thottiyam Trichy India SOET CMR University Department of CSE Bengaluru India Panimalar Engineering College Department of ECE Chennai India

ISBN: (纸本)9798331518943

Crime rates are on the rise, necessitating effective strategies for prevention. machine learning has emerged as a promising tool for forecasting crime incidents due to its ability to learn from data autonomously. The research on the Utilization of machine learning Algorithms in Crime Forecasting investigates innovative approaches to anticipate and diminish crime rates. By utilizing information from the San Francisco Police Department, diverse machine learning algorithms are employed to scrutinize and predict crime patterns. The methodology includes data collection, preprocessing, and the implementation of predictive models like K-Nearest Neighbors (KNN), Support Vector machines (SVM), and Decision Trees. These models were trained on a preprocessed dataset of crime statistics from 2014 to 2017, and their performance was evaluated using accuracy and F-beta score metrics. Key results show that the Decision Tree algorithm achieved the highest accuracy at 88%, with KNN following closely at 87%. SVM, although effective in other scenarios, attained a lower accuracy of 66% in this study. These findings indicate that Decision Trees and KNN are particularly effective for classifying and predicting crime data. © 2024 IEEE.

关键词： Adversarial machine learning

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ESC: Exploration with soft Commonsense Constraints for Zero-shot Object Navigation 40

ESC: Exploration with Soft Commonsense Constraints for Zero-...

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40th international conference on machine learning

作者： Zhou, Kaiwen Zheng, Kaizhi Pryor, Connor Shen, Yilin Jin, Hongxia Getoor, Lise Wang, Xin Eric Univ Calif Santa Cruz Santa Cruz CA 95064 USA Samsung Res Amer Mountain View CA USA

The ability to accurately locate and navigate to a specific object is a crucial capability for embodied agents that operate in the real world and interact with objects to complete tasks. Such object navigation tasks usually require large-scale training in visual environments with labeled objects, which generalizes poorly to novel objects in unknown environments. In this work, we present a novel zero-shot object navigation method, Exploration with soft Commonsense constraints (ESC), that transfers commonsense knowledge in pre-trained models to open-world object navigation without any navigation experience nor any other training on the visual environments. First, ESC leverages a pre-trained vision and language model for open-world prompt-based grounding and a pre-trained commonsense language model for room and object reasoning. Then ESC converts commonsense knowledge into navigation actions by modeling it as soft logic predicates for efficient exploration. Extensive experiments on MP3D (Chang et al., 2017), HM3D (Ramakrishnan et al., 2021), and RoboTHOR (Deitke et al., 2020) benchmarks show that our ESC method improves significantly over baselines, and achieves new state-of-the-art results for zero-shot object navigation (e.g., 288% relative Success Rate improvement than CoW (Gadre et al., 2022) on MP3D).

关键词： Navigation

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