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检索条件"任意字段=7th International Conference on Machine Learning and Soft Computing, ICMLSC 2023"

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Network Intrusion Detection using Deep Reinforcement learning 7

Network Intrusion Detection using Deep Reinforcement Learnin...

引用

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

作者： Sujatha, V. Prasanna, Kodimala Lakshmi Niharika, Kakarla Charishma, Vanukuri Sai, Kamma Bhavya Computer Science and Engineering Vignan's Nirula Institute of Technology and Sciences for Women Guntur India

ISBN: (纸本)9781665464086

the number of internet-connected systems has increased by a huge amount in recent years. these systems are highly vulnerable and increasingly at risk from cyber- attacks. these cyber-attacks need more advanced and intelligent cyber defense systems that include agents that can learn to make judgements on their own without consulting human specialists. Deep learning-based reinforcement learning (DRL) is very good at handling complicated, dynamic, and especially high-dimensional cyber protection problems. this article provides a modern network intrusion detection technology, which employs a deep feed-forward neural network method and reinforcement learning, which is based on Q- learning. In order to detect various sorts of intrusions in the network using an automated trial- and-error method and continually improve its detection skills, the Deep Q-learning (DQL) model is proposed. the accuracy of the model proposed is 91.4%, while the accuracy of other self-taught learning models is 88.4% and it is a similar case for recall rate and precision as well which are 90.2% and 92.8%. Our experimental findings further demonstrate that our suggested DQL beats other comparable machine learning methods and is very good at identifying various intrusion classifications. © 2023 IEEE.

关键词： Reinforcement learning

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Detecting Phishing Websites using machine learning Algorithm 7

Detecting Phishing Websites using Machine Learning Algorithm

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

作者： Kathiravan, M. Rajasekar, V. Parvez, Shaik Javed Sathya Durga, V. Meenakshi, M. Gowsalya, S. Chennai India Computer Science and Engineering SRM Institute of Science and Technology Faculty of Engineering and Technology Vadapalani Chennai India Chennai India

ISBN: (纸本)9781665464086

In general, malicious websites aid the expansion of online criminal activity and stifle the growth of web service infrastructure. therefore, there is a pressing need for a comprehensive strategy to discourage users from going to these sites online. We advocate for a method that uses machine learning to categories websites as either safe, spammy, or malicious. the proposed system is limited to examining the URL itself, rather than the contents of websites. As a result, it does away with both browser-based vulnerabilities and run-time delays. the proposed method outperforms blacklisting services in terms of generality and coverage since it makes use of learning techniques. there are three distinct categories for website addresses. Neutral Web sites provide average, risk-free functionality. For a website, "spam" refers to any attempt to overwhelm the user with advertisements or sites (such as false surveys and online dating sites). Malware is defined as a website designed by hackers to cause harm to computers and steal private data. the experimental data demonstrates a dramatic improvement in performance with the new model compared to the baseline. © 2023 IEEE.

关键词： Decision trees

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Supervised machine learning Approaches for Customer Reviews Sentiment Analysis 7th

Supervised Machine Learning Approaches for Customer Reviews ...

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7th international conference on soft computing: theories and Applications, SoCTA 2023

作者： Garg, Payal Tyagi, Suyash Joshi, Aditya Pandey, Abhijaat Panwar, Deepak GL Bajaj Institute of Technology and Management Greater Noida India Manipal University Jaipur Rajasthan Jaipur India

ISBN: (纸本)9789819720309

Sentiment analysis, also known as opinion mining, is a natural language processing (NLP) method to extract subjectivity and polarity from a body of text, i.e., identify whether the data is positive, negative, or neutral. In this paper, researchers have presented vectorization techniques like Bag of Words, TF-IDF, and HashingVectorizer along with the following text classification algorithms: K-Nearest Neighbour, Multinomial Naïve Bayes, Random Forest Classifier, Decision Tree and SVM. A comparative analysis is performed on the performance of different algorithms and vectorization technique combinations by computing statistical parameter like accuracy, specificity, sensitivity, false positive rate, false negative rate, negative predictive rate, false discovery rate, precision, recall and F1-score. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Sentiment analysis

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Research on Garbage Classification Algorithms Based on Attention Mechanisms and Deep learning 7

Research on Garbage Classification Algorithms Based on Atten...

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7th international conference on machine learning and Natural Language Processing, MLNLP 2024

作者： Yu, Jun Xu, Zhe Han, Ruiyao Bai, Lingnan Fan, Jiaxing Zhang, Zhen Zhang, Yemao Shanxi Taihang Laboratory Co. Ltd. Taiyuan China

ISBN: (纸本)9798350354973

With rapid urbanization and increasing population density, effective waste sorting has become crucial for environmental protection. the diverse nature of waste-varying in type, shape, and background-necessitates precise deep learning models. this study investigates the integration of various attention mechanisms at different positions within the YOLOv8 architecture to enhance waste sorting accuracy. We systematically assess the impact of these attention mechanisms and their specific placements on algorithm performance. Our experimental results not only demonstrate the effectiveness of attention mechanisms in improving mean Average Precision (mAP) but also identify the most effective attention mechanism modules and their optimal locations. Additionally, we propose future research directions, including refining attention mechanism integration strategies and expanding training datasets, to further improve the model's generalization and adaptability to a wider range of real-world scenarios. © 2024 IEEE.

关键词： Adversarial machine learning

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Phyto Leafy Recognition Using Deep learning 7

Phyto Leafy Recognition Using Deep Learning

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2023 7th international conference On computing, Communication, Control And Automation, ICCUBEA 2023

作者： Tapale, Veena Rekhawar, Rutuja Savagonmath, Arpita Habre, Sainath Adhav, Savita Information Technology JSPM's Rscoe Pune411033 India

ISBN: (纸本)9798350304268

Plants serve an essential part in environment by supplying animals with food, O2(oxygen) and shelter. Numerous flora and herb varieties have a lengthy history being utilized as pharmaceuticals and medicinal therapies in conventional medicine. Widely used in folk is still used in many countries (including India) for a number of causes, including the fact that plants and herbs are less expensive than other types of prescription medicines, and plants are harmless and have no adverse effects when used correctly. However, correct identification of therapeutic plant types remains a difficult job in machine learning. Several more electronic systems for greenhouse taxonomic identification have been suggested in recent years, but the majority of these systems only provide acceptable accuracies for a particular and restricted collection of plant species. We create a novel ethnomedicinal identification based on the retrieval of texture and provides multiple color characters from plant leaf pictures in this paper. © 2023 IEEE.

关键词： Boundary extraction Classification of medicinal plants Convolutional Neural Network leaf features Training dataset Deep learning (DL)

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LeafYogi: Deep learning based Mobile Application Framework for Ayurvedic Leaf Classification 7

LeafYogi: Deep Learning based Mobile Application Framework f...

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7th international conference on Inventive Computation Technologies, ICICT 2024

作者： Chalmers, J. thangavel, Senthil Kumar Johnson, I. Amrita Vishwa Vidyapeetham Amrita School of Engineering Department of Electronics And Communication Engineering Coimbatore India Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science And Engineering Coimbatore India Tamil Nadu Agricultural University Department of Plant Pathology Coimbatore India

ISBN: (纸本)9798350359299

Indian forest is the principal depository for useful medicinal herbs. Medical plants along with leaves have medical values and helps in curing many diseases. But correctly identifying leaves is laborious that takes lots of time and expertise. therefore, the identification of these medical species is a challenging task. As a result, vision based approach helps the regular people in rapid identification of herbal leaves. this paper highlights vision based system to recognise herbal leaf by creating advanced deep learning models such as YOLOV5, YOLOV8 and Resnet-50. the Dataset for Indian Medicinal Plant Species Analysis and Recognition (DIMPSAR) which was published in 2023 is used to evaluate deep learning models. the experiment is performed on Colab environment. the evaluation metrics showed that YOLOV8 outperforms the other two models in term of accuracy with 99.2 percentage. the best architecture is further deployed in mobile application using teachable machine. © 2024 IEEE.

关键词： Deep learning

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Detecting Credit Card Frauds Using Deep learning and Face Detection 7

Detecting Credit Card Frauds Using Deep Learning and Face De...

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2023 7th international conference On computing, Communication, Control And Automation, ICCUBEA 2023

作者： Chaudhari, Anagha Sontakke, Atharv Tendulkar, Sakshi Zambare, Sejal Yewale, Mrunali Dept. of Computer Engineering PCCOE Pune India

ISBN: (纸本)9798350304268

In this post-pandemic era, there has been a tremendous rise in online transactions. Along with this increase in frequency, there is an increase in fraudulent transactions. Additionally, a recent Nielsen Report estimates that over the next ten years, the card industry will lose 400 billion to fraud. Techniques of machine learning are frequently used in classifying fraudulent credit card transactions because they operate under the notion that false patterns can be found by looking at previous transactions. But the process becomes difficult when it must take into consideration shifting customer behaviour and fraudsters' inventiveness in creating new fraud patterns. Systems for detecting fraud can use deep learning to uncover anomalies. With the ability to handle both big data and real-time data, deep learning is also superior to current machine learning techniques. We aim to provide a solution to solve this problem of credit card fraud detection by leveraging the technology of face detection and combining it with deep learning, thus proposing a two-step verification-based approach. Broadly, in the proposed approach, the user has to register using his card details and then capture his face. then login to the system, enter the card details and the system will prompt for face capture once the face and the card details are captured they are verified with the already stored data in the database. If the verification passes then the transaction is successful otherwise the user is logged out of the system. © 2023 IEEE.

关键词： credit card deep learning face detection fraud Haar cascade

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Performance Analysis of machine learning Algorithm for the Credit Risk Analysis in the Banking Sector 7

Performance Analysis of Machine Learning Algorithm for the C...

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

作者： Hegde, Sandeep Kumar Hegde, Rajalaxmi Krishna Priya, R. Sree Southry, S. Marthanda, A.V.G.A. Logu, K. Karkala Taluk Udupi India Department of Engineering Electrical Section University of Technology and Applied Sciences Shinas campus PO Box 66Al-Aqur Shinas324 Oman Department of ECE Sona College of Technology Salem India Department of EEE Laki Reddy Bali Reddy College of Engineering Mylavaram Permanently Affiliated to JNTU Andhrapradesh Kakinada India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Tamilnadu Chennai India

ISBN: (纸本)9781665464086

the banking sector has advanced in recent years. thus, there is an increase in the demand for bank loans. the bank must distribute and sell each of the limited number of available slots to a select group of people. As a result, a usual stage is to identify who will be unable to return the loan and who will prove to be a more trustworthy option to the bank. In order to save the bank time and costs, in the proposed paper machine learning based approach is introduced to reduce the risk involved with finding the safe individual. In order to decide whether or not to grant someone a loan, this paper presents a method of loan approval based on predetermined criteria. the machine learning model for credit approval was implemented using logistic regression, XG Boost, random forest and naïve bayes model. the experimental results indicates that logistic regression model is more accurate for the credit risk analysis. © 2023 IEEE.

关键词： Python

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Monitoring and Enhancing Employee's Work Efficiency Using Wearable Device 7

Monitoring and Enhancing Employee's Work Efficiency Using We...

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2023 7th international conference On computing, Communication, Control And Automation, ICCUBEA 2023

作者： Mehta, Deevya Savla, Jenil Shah, Shubham Jaiswal, Anish Mangrulkar, Ramchandra Dwarkadas J. Sanghvi College of Engg. Department of Computer Engineering Mumbai India

ISBN: (纸本)9798350304268

this research article focuses on development of an employee efficiency monitoring system. the aforementioned system comprises an IoT based wearable which tracks an employee throughout their working hours to report their attendance. this along with other factors such as projects and task submissions are included in a formula to devise an efficiency value for Employee which can be tracked by admin. Current article represents an extension to existing office management interface by including an Employee Recognition Mechanism which calculates the reward points for each employee so top performers can be rewarded. A ML based Workload balancing feature is also implemented which ensures even distribution of tasks for employees. © 2023 IEEE.

关键词： Attendance Management Employee IoT machine learning Micro-controller Work efficiency

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From Data to Decisions: Big Data Analytics in Action 7

From Data to Decisions: Big Data Analytics in Action

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2023 7th international conference On computing, Communication, Control And Automation, ICCUBEA 2023

作者： Light, Juzer Kshirsagar, Pranali Shinde, Gitanjali Katikar, Shilpa Mahajan, Rupali Mehta, Pradnya Viit Department of Computer Science Engineering Pune India

ISBN: (纸本)9798350304268

In the age of data-driven decision making, big data analytics has become a crucial area of study and application. Organisations are increasingly resorting to big data analytics to get important insights, gain a competitive edge, and spur innovation as a result of the surge in data from numerous sources, including social media, loT devices, and transactional records. We present a thorough analysis of the major theories, techniques, and applications of big data analytics in this research article. this research paper also present Big Data Analytics case study. We go over the difficulties and possibilities presented by big data analytics, emphasise the current status of study and use, and suggest future lines of inquiry. © 2023 IEEE.

关键词： Analytics Big Data machine learning

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