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IEEE Symposium on Intelligent Vehicle

作者： Tairui Chen Zhilu Chen Quan Shi Xinming Huang Worcester Polytechnic Institute Worcester MA USA Nantong University Nantong Jiangsu China

This paper presents a novel approach for road marking detection and classification based on machine learning algorithms. Road marking recognition is an important feature of an intelligent transportation system (ITS). Previous works are mostly developed using image processing and decisions are often made using empirical functions, which makes it difficult to be generalized. Hereby, we propose a general framework for object detection and classification, aimed at video-based intelligent transportation applications. It is a two-step approach. The detection is carried out using binarized normed gradient (BING) method. PCA network (PCANet) is employed for object classification. Both BING and PCANet are among the latest algorithms in the field of machine learning. Practically the proposed method is applied to a road marking dataset with 1,443 road images. We randomly choose 60% images for training and use the remaining 40% images for testing. Upon training, the system can detect 9 classes of road markings with an accuracy better than 96.8%. The proposed approach is readily applicable to other ITS applications.

关键词： Roads Principal component analysis Object detection Training machine learning algorithms Feature extraction Neural networks

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Predicting breast cancer survivability based on machine learning and features selection algorithms: a comparative study

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JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING 2021年第8期12卷 8585-8623页

作者： El Rahman, Sahar A. Princess Nourah Bint Abdulrahman Univ Coll Comp & Informat Sci Comp Sci Dept Riyadh Saudi Arabia Benha Univ Fac Engn Shoubra Elect Engn Dept Cairo Egypt

Breast cancer (BC) is considered the most common cause of cancer deaths in women. This study aims to identify BC early based on machine learning algorithms and features selection methods. The overall methodology of this work was modified based on knowledge data discovery (KDD) process, which include four datasets, preprocessing phase (data cleaning, data splitting to training and testing sets), processing phase (feature selection, k-folds validation, and classification) and finally model evaluation. This paper presents a comparison between different classifiers such as decision tree (DT), random forest (RF), logistic regression (LR), Naive Bayes (NB), K-nearest neighbor (KNN), and support vector machine (SVM). Four different breast cancer datasets (Wisconsin prognosis breast cancer (WPBC), Wisconsin diagnosis breast cancer (WDBC), Wisconsin Breast Cancer (WBC), and Mammographic Mass Dataset (MM-Dataset) based on BI-RADS findings) are conducted in the experiments. The proposed models were evaluated by utilizing classification accuracy and confusion matrix. The experimental results indicate that the classification based on RF technique with the Genetic Algorithm (GA) as a feature selection method is better than the other classifiers with an accuracy value 96.82% using WBC dataset. In WDBC dataset, the results indicate that the classification utilizing C-SVM technique with the applied kernel function RBF (Radial Basis Function) is superior to the other classifiers with an accuracy value 99.04%. In WPBC dataset, the results indicate that the classification using RF technique with recursive feature elimination (RFE) as a feature selection method is better than the other classifiers with an accuracy value 74.13%. In MM-Dataset, the results indicate that the classification using DT technique is better than the other classifiers with an accuracy value 83.74%. The findings indicate that the proposed models are effective by comparing with others existing models.

关键词： BI-RADS Breast cancer (BC) Classification Feature selection machine learning algorithms WBC WDBC WPBC

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Differentially Private ADMM algorithms for machine learning

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IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY 2021年 16卷 4733-4745页

作者： Shang, Fanhua Xu, Tao Liu, Yuanyuan Liu, Hongying Shen, Longjie Gong, Maoguo Xidian Univ Key Lab Intelligent Percept & Image Understanding Minist Educ Sch Artificial Intelligence Xian 710071 Peoples R China Peng Cheng Lab Shenzhen 518066 Peoples R China Xidian Univ Key Lab Intelligent Percept & Image Understanding Minist Educ Sch Elect Engn Xian 710071 Peoples R China

In this paper, we study efficient differentially private alternating direction methods of multipliers (ADMM) via gradient perturbation for many centralized machine learning problems. For smooth convex loss functions with (non)-smooth regularization, we propose the first differentially private ADMM (DP-ADMM) algorithm with the performance guarantee of (epsilon, delta)-differential privacy ((epsilon, delta)-DP). From the viewpoint of theoretical analysis, we use the Gaussian mechanism and the conversion relationship between Renyi Differential Privacy (RDP) and DP to perform a comprehensive privacy analysis for our algorithm. Then we establish a new criterion to prove the convergence of the proposed algorithms including DP-ADMM. We also give the utility analysis of our DP-ADMM. Moreover, we propose a new accelerated DP-ADMM (DP-AccADMM) algorithm with the Nesterov's acceleration technique. Finally, we conduct numerical experiments on many real-world datasets to show the privacy-utility tradeoff of the two proposed algorithms, and all the comparative analysis shows that DP-AccADMM converges faster and has a better utility than DP-ADMM, when the privacy budget epsilon is larger than a threshold.

关键词： Privacy Perturbation methods Differential privacy Convergence Approximation algorithms Convex functions machine learning algorithms Differentially private alternating direction method of multipliers (ADMM) gradient perturbation momentum acceleration Gaussian mechanism

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Cybersecurity With machine learning: Implementing AI algorithms for Intrusion Prevention, Advanced Data Protection, and Real-Time Threat Analysis

Cybersecurity With Machine Learning: Implementing AI Algorit...

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Computational, Communication and Information Technology (ICCCIT), International Conference on

作者： Krishna Bonagiri P. Krishnamoorthy V. Keerthiga D. Kirubakaran Rajesh David B. Nancharaiah Quadrant Technologies Redmond WA USA Department of Computer Science and Engineering Sasi Institute of Technology & Engineering Andhra Pradesh India Department of CSE Anand Institute of Higher Technology Chennai Kazhipattur India Department of Computer Science and Engineering R.M.K. Engineering College Chennai Tamil Nadu India KUKA U.S. Holdings Company LLC Austin TX USA Department of ECE Usha Rama College of Engineering and Technology Telaprolu A.P. India

ISBN: (数字)9798331512965

ISBN: (纸本)9798331512972

In an era where cyber attacks are getting increasingly complex, the incorporation of machine learning (ML) algorithms into cybersecurity processes has emerged as a crucial technique for boosting protection against potential intrusions and data breaches. For the purpose of preventing intrusions, providing superior data protection, and conducting real-time threat analysis, this research investigates the application of machine learning approaches that are driven by artificial intelligence. Organizations are able to increase their preventative defenses against cyber attacks by utilizing algorithms that are capable of analyzing massive volumes of data and identifying patterns that are indicative of harmful behavior. Several machine learning (ML) approaches, including supervised and unsupervised learning, anomaly detection, and neural networks, are investigated in this study. The purpose of the study is to demonstrate the usefulness of these approaches in identifying and responding to a wide range of security concerns. In addition to this, the research investigates the significance of continual learning and adaptation in machine learning models, which enables these models to develop alongside developments in new threats. The findings highlight the potential for machine learning to change cybersecurity frameworks by delivering speedy and automatic reactions to incidents. This would lead to the protection of sensitive information and an overall improvement in the system's resilience. In the end, this research highlights the importance of implementing tactics that are based on artificial intelligence in order to meet the intricacies of modern cyber threats and maintain robust security in a world that is becoming increasingly digital.

关键词： Ethics machine learning algorithms Prevention and mitigation Neural networks Data protection machine learning Real-time systems Computer security Protection Unsupervised learning

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Comparative evaluation of the performance and effectiveness of machine learning and Deep learning algorithms for anomaly detection in Android log analysis

Comparative evaluation of the performance and effectiveness ...

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International Conference on Advanced Communication Technology (ICACT)

作者： Okangondo Loshima Junior Serigne Modou Kara Samb Mohamed Kaba Keita Doudou Fall Moussa Dethie Sarr Idy Diop Faculty of Science and Technology University Cheikh Anta Diop of Dakar Dakar Senegal Science Technology Mathematics Computer Science Iba Der Thiam University Thies Senegal Ecole Supérieure Polytechnique University Cheikh Anta Diop of Dakar Dakar Senegal

ISBN: (数字)9791188428137

ISBN: (纸本)9798331507602

Due to the substantial rise in cyberattacks aimed at Android devices, log analysis has become critical for identifying and mitigating security anomalies. This article conducts a comparative analysis of machine learning and deep learning algorithms for the detection of anomalies within Android logs, specifically sourced from the LogHub dataset. These logs, gathered from Android smartphones equipped with extensive instrumentation, are both rare and intricate. The complexity arises from Android’s multithreaded architecture, making anomaly detection a challenging *** aim of this study is to identify the most suitable algorithms for detecting anomalies in Android logs, evaluating three algorithms per category (machine learning and deep learning). Using metrics such as precision, recall, F1 score and AUC-ROC, the analysis highlights the effectiveness of each approach depending on context. Finally, recommendations are proposed for optimizing current methodologies and exploring new research perspectives.

关键词： Deep learning Support vector machines machine learning algorithms Accuracy Forestry Security Reliability Object recognition Anomaly detection Smart phones

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machine learning for Wireless Network Optimization: algorithms and Applications for Adaptive Communication

Machine Learning for Wireless Network Optimization: Algorith...

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Advances in Computer Science, Electrical, Electronics, and Communication Technologies (CE2CT), International Conference on

作者： Madhira Srinivas S. Surya Prachi Deepak Thakar Amit Kumar Jain Department of Computer Science & Engineering Data Science Geethanjali College of Engineering & Technology (GCET) Hyderabad India Sri Ramachandra Faculty of Engineering & Technology (SRET) Sri Ramachandra Institute of Higher Education & Research Porur Chennai India Department of Computer Engineering KJ college of Engineering & Management Research Pune India Department of Electrical & Electronics Engineering Poornima University Jaipur Rajasthan India

ISBN: (数字)9798331518578

ISBN: (纸本)9798331518585

This project looks into the possibility of applying machine learning to optimize wireless networks for adaptive communication. Using 5G resource data, it applies preprocessing, exploratory analysis, and visualization to identify key trends: signal strength, latency, and bandwidth utilization. machine learning algorithms predict resource allocation based on latency-sensitive and bandwidth-demanding applications. The approach emphasizes energy efficiency, scalability, and service quality, and dynamically adjusts communication strategies to meet diverse network demands. The findings indicate the machine learning importance in enhancing elasticity and robustness in a wireless network and its general function.

关键词： machine learning algorithms Adaptive systems Wireless networks Scalability machine learning Bandwidth Predictive models Prediction algorithms Resource management Optimization

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Predictive Analysis of machine learning and Deep learning algorithms for Lung Disease Prediction

Predictive Analysis of Machine Learning and Deep Learning Al...

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International Conference on Inventive Computation Technologies (ICICT)

作者： Raji Ramachandran Ashina A Jawad Amal U J Department of Computer Science and Applications Amrita School of Computing Amrita Vishwa Vidyapeetham Amritapuri India

ISBN: (数字)9798331512248

ISBN: (纸本)9798331512255

Lung cancer is a significant health issue all over the world, and thus requires effective and precise solutions. Traditionally, lung cancer detection relied on primitive methods such as radiology examination of CT scans, which most of the time leads to delayed diagnosis. These primitive methods were error-prone, inconsistent, and might result in inaccurate results, hence making them hard. This paper discusses predictive analysis of machine learning and deep learning algorithms for prediction of lung cancer. In our approach, using both CSV data and CT scan images, we assess the accuracy and efficiency of different machine learning and deep learning models. While deep learning algorithms process images, machine learning algorithms are applied in CSV data. The paper combines machine learning and deep learning models with hyperparameter tuning to achieve the systems best performance. Evaluation metrics, such as accuracy, recall, precision, and F1-score, are utilized in the evaluation of the comparison purpose. Through model tuning and performance evaluation, the system seeks to uncover the most effective and efficient prediction model.

关键词： Deep learning machine learning algorithms Accuracy Computed tomography Computational modeling Lung cancer Predictive models Prediction algorithms Predictive analytics Tuning

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Enhancing Meal Recommendation algorithms: A Contextual Approach to machine learning Calibration

Enhancing Meal Recommendation Algorithms: A Contextual Appro...

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International Conference on Computer and Communications Technologies (ICCCT)

作者： M. Balamurugan Keshav A Viswanathan S Dinesh R Department of Computer Science and Engineering Sri Sai Ram Engineering College Chennai India

ISBN: (数字)9798331537579

ISBN: (纸本)9798331537586

Personalization has remained a very crucial aspect of present-day business strategies as corporations begin to increasingly use the data and machine learning to personalize experiences towards individual users. This paper explores designing a food recommendation system catering to each user's personalized preferences. The system assimilates various inputs gathered from different users, incorporating dietary restrictions, time, and prevailing weather conditions, while facilitating context-aware recommendations coherent with the meal choices adopted by the users. These two methodologies are core to this system: content-based filtering and collaborative filtering. The content-based filtering methodology suggests items based on the characteristics of food items, which might be similar to previously enjoyed dishes by the user. The algorithm suggests meals based on the specific features of the user's taste profile, thus ensuring that suggestions are highly relevant to his established preferences. In contrast, collaborative filtering is an even broader approach to analyze the patterns and selection habits of other users within the system. It identifies individuals who have the same taste profile and uses their selections to make recommendations for food. Based on the group of collective preferences among all the users, collaborative filtering will expose the new user to dishes they might never have discovered but will most probably enjoy since they are derived from common tastes. To make the personalization of food recommendations even more specific, the system integrates k-nearest neighbors, commonly known as KNN. It measures the similarity between the users based on their previous interactions, ratings for their meals, and preferences and selects the “k” users whose tastes are the closest to those of the current user, thus making a cluster of like-minded people. It checks out the favorite food items of those neighboring users to produce suggestions that resonate with thi

关键词： machine learning algorithms Filtering Collaborative filtering Current measurement machine learning Nearest neighbor methods Real-time systems Communications technology Recommender systems Meteorology

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A Review on the Applications of machine learning and Deep learning algorithms for Image Recognition

A Review on the Applications of Machine Learning and Deep Le...

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Sentiment Analysis and Deep learning (ICSADL), International Conference on

作者： Ketan Wanjari Prateek Verma Department of Computer Science and Engineering Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research (Deemed to be University) Sawangi (Meghe) Wardha Maharashtra India Department of Artificial Intelligence and Data Science Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research (Deemed to be University) Sawangi (Meghe) Wardha Maharashtra India

ISBN: (数字)9798331523923

ISBN: (纸本)9798331523930

Modern image recognition has experienced dramatic improvements because of machine learning and Deep learning algorithms together. This study investigates CNNs and SVMs for recognition enhancement while reviewing image recognition technologies extensively through literature. This paper demonstrates how the applications of healthcare and security systems and social media analysis influence society. New developments in technology have not resolved multiple unresolved obstacles such as data bias as well as computational complexity and privacy concerns together with real-time processing restrictions. The latest image processing techniques include ViTs alongside GANs and Few-Shot learning but developers need to achieve better results in future improvements. The main goal of this research examines present-day advancements in ML and DL with a review of their capabilities as well as constraints before recommending future study paths to overcome problems encountered today. This paper evaluates both the future potential and benefits alongside drawbacks of ML and DL models applied to image recognition.

关键词： Deep learning Ethics Data privacy Image recognition machine learning algorithms Reviews Social networking (online) Vectors Security Stakeholders

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machine learning-Driven Path Planning and Obstacle Avoidance algorithms for Autonomous Vehicles

Machine Learning-Driven Path Planning and Obstacle Avoidance...

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Computer Applications and Information Technology (ISCAIT), International Symposium on

作者： Daoyi Xiao Lekang Wang Yanghui Jing Bolun Li School of Electronics and Control Engineering Chang’an University Xi’an China

ISBN: (数字)9798331542856

ISBN: (纸本)9798331542863

With the rapid advancement of autonomous driving technology, path planning and obstacle avoidance strategies based on machine learning are of great significance for the efficient and safe operation of autonomous vehicles. In order to achieve automatic driving path planning and obstacle avoidance, this paper combines deep reinforcement learning and probability graph model to propose a complex method. First, Deep Q Network (DQN) perceives and decides the surrounding environment to obtain real-time information about road conditions, obstacle locations and other traffic participants. Markov Decision Process (MDP) etc are introduced to further perform path planning and dynamic obstacle prediction, optimize the trajectory selection and obstacle avoidance strategy of vehicles. The experimental results demonstrate that the proposed method achieves a much superior performance in the complex urban road environment, which can significantly enhance the operating precision and traffic safety of an autonomous vehicle. The suggested approach has validated terrestrial experiments in root tests, providing tremendous enhancements in navigation accuracy and measures of traffic safety for autonomous vehicles.

关键词： machine learning algorithms Roads Programming Probabilistic logic Deep reinforcement learning Path planning Safety Collision avoidance Vehicle dynamics Autonomous vehicles

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