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Handwritten Digits Recognition using Machine and Deep learning 18

Handwritten Digits Recognition using Machine and Deep Learni...

引用

18th INDIAcom;11th international conference on computing for Sustainable Global Development, INDIACom 2024

作者： Mittal, Vishul Sharma, Shilpa Thapar, Puneet Lovely Professional University Department of Computer Science and Engineering Punjab Phagwara India

ISBN: (纸本)9789380544519

Information processing has shown a great deal of use for handwritten digit recognition. However, because people's writing styles vary so much, correctly identifying these characters from photos is a challenging undertaking. Furthermore, this procedure is complicated by the presence of various visual effects such as noise, blurring, and intensity changes. In our proposed approach, we have address all these limitations by introducing a deep learning-based technique. The model is trained on these images in order to identify and classify the numerals from zero to nine. Our model showed an average accuracy value of 99.81%.The experimental findings demonstrate that, in spite of different writing styles such as noise, blurring and changes in number position and size, our technique is able to reliably classify Handwritten Digit Recognition from photographs. Additionally, the provided method can generalized effectively to scenarios that have not yet been seen, indicating that the model is a useful solution for numeral recognition. © 2024 Bharati Vidyapeeth, New Delhi.

关键词： Deep learning Handwritten Digits Information Processing Machine learning Recognition from Photographs

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Decentralized Multi-UAV Management in Mobile Edge computing: A Hierarchical Reinforcement learning Approach 3

Decentralized Multi-UAV Management in Mobile Edge Computing:...

引用

3rd international conference on computing, Communication, Perception and Quantum Technology, CCPQT 2024

作者： Peng, Ziling Liu, Shuai Jaber, Al Jian, Lim W.E.I. Li, Hui Qian, Jin College of Information Engineering Taizhou University Taizhou China

ISBN: (纸本)9798331528386

The integration of drones in Mobile Edge computing (MEC) has opened new possibilities for enhancing network performance in various fields, such as agriculture, environmental monitoring, and disaster management. However, effective coordination of multiple Unmanned Aerial Vehicles (UAVs) in dynamic and distributed environments poses significant challenges, especially in mobility management and resource allocation. To address these issues, we propose a Decentralized Hierarchical Policy Framework inspired by hierarchical reinforcement learning. This framework employs a two-level structure, consisting of high-level communication coordinators and low-level executors, allowing UAVs to make decentralized decisions based on local observations and partial communication. Our approach models the multi-UAV MEC problem as a Decentralized Partially Observable Markov Decision Process with communication, enabling efficient task allocation and resource management. Experimental evaluations demonstrate that the proposed method improves MEC service coverage, reduces latency, and optimizes UAV energy consumption. Compared to state-of-the-art Multi-Agent Reinforcement learning methods, our approach achieves superior performance in dynamic and large-scale environments. © 2024 IEEE.

关键词： Reinforcement learning

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Securing SDN Networks: Employing LSTM and Linear SVM Models for Enhanced Network Security with VPN Integration 15

Securing SDN Networks: Employing LSTM and Linear SVM Models ...

引用

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

作者： Baji, Sotsava Skandhaa Suryadevara, Kranthi Bodapati, Viswanath Velagapudi Ramakrishna Siddhartha Engineering College Department of Information Technology Vijayawada India

ISBN: (纸本)9798350370249

In the ever-changing landscape of network security, Software-Defined Networking (SDN) is a critical foundation that requires strong protections against cyber threats. This research describes a novel strategy to fortifying SDN networks by combining Deep learning (DL) and Machine learning (ML) approaches. Our solution detects, analyses, and prevents potential security breaches in real time by using Long Short-Term Memory (LSTM) and Linear Support Vector Machine (SVM) models, as well as PyVPN integration. Our technology intends to improve SDN network resilience against a variety of cyber threats, including malware, intrusions, and denial-of-service attacks, by analysing network traffic patterns comprehensively and proactively identifying anomalies. Through extensive validation, we demonstrate the usefulness of our strategy in strengthening SDN network security, providing a robust defense mechanism against the ever-persistent threat landscape. © 2024 IEEE.

关键词： Anomaly Detection Cyber Threats Deep learning Linear SVM LSTM Machine learning Network Security PyVPN SDN

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Anomalous Behavior Detection in ATM using Different Methodologies: A Review 3

Anomalous Behavior Detection in ATM using Different Methodol...

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3rd international conference on Automation, computing and Renewable Systems, ICACRS 2024

作者： Thakre, Harsh Bhushanwar, Kush Patel, Anil Computer Engineering Department PIET Parul University Gujarat Vadodara India

ISBN: (纸本)9798331532420

The necessity for standardized anomaly detection systems in financial transactions is driven by the alarming increase in ATM fraud and criminal activities. Many techniques for detecting anomalous behavior in ATM systems are also summarized and evaluated in this review article. To better secure financial institutions and customers from fraudsters, it is crucial to examine the reasoning behind these systems, particularly in the areas that pertain to improving security. The overarching purpose of this paper is to present a clear and succinct explanation of the cutting-edge machine learning (ML) and deep learning (DL) algorithms that are presently the backbone of ATM anomaly pattern detection, including more contemporary methods like transfer learning. This review delves into the theoretical foundations of these strategies and compares different algorithms, along with the values of performance indicators that show their strengths and drawbacks. Our goal is to show, via an examination of current methods in actual settings, any flaws or problems that may hinder the development of ATM security in the future. Finally, this work can be used by researchers and those interested in bettering ATM systems and associated secure technologies in the future to study the subject and explore anomaly detection technologies as a means of both practical experience and future advancement. © 2024 IEEE.

关键词： Adversarial machine learning

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Convolutional Neural Network-based image tamper detection with Error Level Analysis 2

Convolutional Neural Network-based image tamper detection wi...

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2nd IEEE international conference on Intelligent and Innovative Technologies in computing, Electrical and Electronics, ICIITCEE 2024

作者： Manjunatha, S. Swetha, M.D. Rashmi, S. Subramanian, Ananda Kumar Vinoth Kumar, V. Mallikarjuna Swamy, S. Global Academy of Technology Department of Information Science and Engineering Bengaluru India BNM Institute of Technology Department of Computer Science and Engineering Bengaluru India Bengaluru India Vit - University School of Computer Science Engineering Dept of Computational Intelligence Vellore India Vit - University School of Computer Science Engineering and Information Systems Vellore India Jss Academy of Technical Education Dept of Electronics and Communication Engineering Bengaluru India

ISBN: (纸本)9798350306415

Photography is the most important, powerful, and reliable means of expression. Today, digital images not only provide disinformation but also act as agents for secret communication. Users and editing professionals work with digital images for a variety of purposes. Images are often regarded as facts or proof of reality, so they are misleading and fake news or publications of any form that use images manipulated in a highly misleading way. To recognize image tampering needs multiple image data and a model that can handle all the pixels in the image. Furthermore, training the data more efficiently and needed flexibility support everyday use. Models based on Deep learning such as Convolutional Neural Networks with error level analysis (ELA) are the perfect solution. © 2024 IEEE.

关键词： Convolutional Neural Network Deep learning Efficiency Error Level Analysis Image Forgery Detection

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Comparative Evaluation of Machine learning Techniques for Breast Cancer Detection

Comparative Evaluation of Machine Learning Techniques for Br...

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2024 international conference on Electrical, Electronics and computing Technologies, ICEECT 2024

作者： Sharma, Atul Pati, Natasha Gourisaria, Mahendra Kumar Panda, Amiya Ranjan Patra, Sudhansu Shekhar Pattnayak, Parthasarathi KIIT Deemed to be University School of Computer Engineering Odisha Bhubaneswar India KIIT Deemed to be University School of Computer Applications Odisha Bhubaneswar India

ISBN: (纸本)9798350378092

Breast cancer is a primary cause of death for women. Millions of women worldwide were suspected with breast cancer diagnosis in 2022. But early-stage breast cancer is still treatable. Patients' recoveries become better when breast cancer is discovered early and treated quickly and efficiently. Machine learning algorithms have become an effective tool for precisely and successfully diagnosing breast cancer in recent years. This research focuses on the utilization of machine learning (ML) techniques. To achieve this, we looked into a variety of machine learning methods including XGBoost, Random Forest, K Nearest Neighbours, Naïve Bayes, Decision Trees, Logistic Regression, and Neural Networks. Metrics including accuracy, precision, F1score, recall, and balanced accuracy were used as indicators of the models. The result shows that K Nearest Neighbors and Naïve Bayes has more accuracy and can be effective in early detection of cancer. © 2024 IEEE.

关键词： Lung cancer

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An Analysis of Privacy Attacks in Supervised Machine learning Algorithms 1

An Analysis of Privacy Attacks in Supervised Machine Learnin...

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1st international conference on computing, Sciences and Communications, ICCSC 2024

作者： Gupta, Kavya Gupta, Anurag Raipoot, Abha Kiran Ghaziabad India Ghaziabad India School of Computer Science & Engineering Galgotias University Greater Noida India

ISBN: (纸本)9798350353648

With the widespread integration of machine learning (ML) into diverse applications, it has become paramount to scrutinize its implications for security and privacy. Despite the burgeoning body of work in privacy, there is a noticeable gap in research attention between the security and privacy aspects of ML, specifically supervised machine learning (S-ML). This paper bridges this gap by analyzing research papers focusing on privacy attacks against S-ML over the past years. This study offers a comprehensive overview of prevalent defense mechanisms and initiates a thoughtful discussion on unresolved challenges and prospective research directions. As ML continues to permeate various domains, the scientific community's interest has expanded beyond performance metrics to encompass broader concerns such as security, privacy, fairness, and explainability. This survey investigates state-of-the-art privacy-related attacks, elucidating common design patterns and distinctive characteristics. Through this exploration, we identify several open problems that demand further investigation, emphasizing the critical need to fortify privacy frameworks alongside the escalating ubiquity of ML technologies. © 2024 IEEE.

关键词： Adversarial machine learning

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Citation Prediction Analysis Using Deep learning Techniques 15

Citation Prediction Analysis Using Deep Learning Techniques

引用

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

作者： Mahalakshmi, G.S. Vinitha Kumari, R. Jaysankar, Pranav Sendhilkumar, S. Anna University Department of Computer Science and Engineering Chennai India Anna University Department of Information Science and Technology Chennai India

ISBN: (纸本)9798350370249

In scientific research, the evaluation of significant articles has traditionally relied on metrics such as Journal Impact Factor and citation counts, but these measures often miss the true essence of an article's impact. But this viewpoint has changed with the introduction of approaches for machine learning and text mining, which let academics examine the subtleties in an article's semantics and so obtain a deeper comprehension of their content. Consequently, the impact of an article's citation count is increasingly driven by its quality and substantive contribution rather than mere popularity metrics. By prioritizing the semantic richness of research articles, scholars can make more informed decisions about which works to engage with and follow, thereby enriching their own research endeavors. This research investigates differences in analysis techniques for deep-text and full-text articles by identifying trends and conducting semantic analysis. Furthermore, the number of citations for every article is predicted using a variety of deep learning techniques. © 2024 IEEE.

关键词： Adversarial machine learning

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Effectiveness of Class Imbalance Treatment for Diagnosis of PCOS Using Machine learning Models: A Comparative Study 10

Effectiveness of Class Imbalance Treatment for Diagnosis of ...

引用

10th international conference on Advanced computing and Communication Systems, ICACCS 2024

作者： Majumdar, Sounak Biswas, Saroj Kr. Purkayastha, Biswajit National Institute of Technology Silchar Department of Computer Science and Engineering Silchar India

ISBN: (纸本)9798350384369

Polycystic Ovary Syndrome (PCOS) is the most prevalent hormonal disorder among women during their reproductive years. It is one of the leading causes of female infertility. Diagnosing peos is difficult because the symptoms of PCOS vary widely and there is no single medical test that can accurately diagnose PCOS. This encourages researchers to develop data driven models for PCOS diagnosis. However, a very common challenge in the data driven approach for medical diagnosis is the problem of imbalanced dataset. This paper presents a comparative study on the effectiveness of different oversampling techniques in handling the class imbalance problem for PCOS diagnosis. This study has explored six oversampling techniques in combination with thirteen different machine learning models which includes eight single classifier-based models and five ensemble models. The dataset used for this study is available on Kaggle machine learning repository. Experimental results shows the effectiveness of class imbalance treatment for PCOS diagnosis. Highest accuracy of 92.9% has been achieved by the model that uses Gaussian SMOTE for oversampling and Extra Trees for classification. © 2024 IEEE.

关键词： Contrastive learning

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Image-Based Cognitive States Identification through Deep learning 2

Image-Based Cognitive States Identification through Deep Lea...

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2nd IEEE international conference on Intelligent and Innovative Technologies in computing, Electrical and Electronics, ICIITCEE 2024

作者： Kundan Rao, S. Ambannavar, Siddarth Sinha, G.R. Bms College of Engineering Dept. of Machine Learning Bangalore560019 India Gfsc University Gujarat Vadodara India

ISBN: (纸本)9798350306415

Yoga and mindfulness practices offer a variety of benefits, promoting both physical and mental well-being. This research intersects with AI, psychology, neuroscience, and more within cognitive science, contributing to the study of the brain, mind, memory, and disorders. The study presents a novel approach utilizing deep learning and image classification techniques to distinguish practitioners from non-practitioners of yoga based on pre-processed data. Data pre-processing involves down sampling, filtering, and Independent Component Analysis (ICA) to enhance the extraction of relevant features. The pre-processed data is transformed into images using Gramian Angular Field, facilitating the utilization of Convolutional Neural Network (CNN) models. The proposed methodology leverages custom-built CNN models to classify individuals into their respective groups. The models are trained on the image representations of the pre-processed data, enabling the extraction of intricate patterns and features for accurate classification. © 2024 IEEE.

关键词： Deep learning EEG Gramian Angular Field (GAF) Image Classification Mindfulness

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