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检索条件"任意字段=International Conference on Advances in Machine Learning and Systems Engineering"

共 69114 条记录，以下是311-320 订阅

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machine learning Method for Reliable Malware Detection in IoT using Opcode Category Features 2

Machine Learning Method for Reliable Malware Detection in Io...

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2nd international conference on advances in Computation, Communication and Information Technology, ICAICCIT 2024

作者： Pooja, S. Venkatesh, T. Reethika, A. Rithanya, E. Vinesha, T.V. Priyadharshini, R. Department of Electronics and Communication Engineering KPR Institute of Engineering and Technology Coimbatore India Department of Electronics and Communication Engineering Sri Ramakrishna Engineering College Coimbatore India Department of Information Technology Sri Ranganathar Institute of Engineering and Technology Coimbatore India

ISBN: (纸本)9798331541217

The number of threats has grown as a result of the Internet of Things' (IoT) rapid expansion, making effective malware identification and classification techniques necessary. The goal of this project is to improve IoT malware detection systems (MDS) and categorization by utilizing machine learning techniques and opcode category properties. Using neural network methods, the method examines opcode sequences taken out of IoT device firmware. These sequences are converted into features of the opcode category, which are then fed into the CNN model. By means of comprehensive training and assessment, the model acquires the ability to differentiate between benign and dangerous opcode patterns, hence facilitating precise malware identification and classification. The malware identification and categorization system that utilizes CNN technology demonstrates encouraging performance metrics, attaining elevated levels of accuracy, precision, and recall. In particular, the model outperforms other machine learning techniques as well as conventional signature-based methods in identifying and classifying various forms of IoT malware. The results of this study highlight how well CNN algorithms and opcode category features work together to provide reliable IoT malware identification and classification. This method strengthens defense against new hazards in the IoT ecosystem and improves cybersecurity measures by precisely recognizing malicious behaviors within the firmware of IoT devices. © 2024 IEEE.

关键词： Firmware

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Smart Manufacturing: Integrating IoT and AI for Agile Production Management 1

Smart Manufacturing: Integrating IoT and AI for Agile Produc...

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

作者： Annavarapu, Babu Jagadeesh Bastray, Tejaswini Rusho, Maher Ali Long, Choi Sang Channi, Arvinder Singh Kumar, Vishwa Vijay Hyderabad India St Joseph's University Department of Management India University of Colorado Lockheed Martin Engineering Management BoulderCO United States Raffles University Malaysia Guru Kashi University Talwandi Sabo Punjab Bathinda India Stuart School of Business Illinois Institute of Technology ChicagoIL United States

ISBN: (纸本)9798331518578

Production management is being revolutionised by smart manufacturing, which is fuelled by the combination of artificial intelligence (AI) and the Internet of Things (IoT) and allows for quick, data-driven decision-making. Through this interface, producers may use cutting-edge machine learning models and real-time sensor data for process optimisation, quality assurance, and predictive maintenance. Model performance is evaluated using evaluation metrics like accuracy, recall, ROC curves, and AUC in conjunction with machine learning approaches including classification, regression, and time series forecasting. AI-driven systems can enhance production scheduling, anticipate equipment problems, and optimise resource allocation by utilising IoT data. This makes it possible to create agile manufacturing environments that can minimise downtime and react swiftly to changes in demand. The robustness and dependability of these AI models are further improved by the addition of machine learning assessment techniques including learning curves, feature importance, and cross-validation. In the end, this IoT and AI synergy enables manufacturers to attain increased productivity, cost reductions, and adaptability in dynamic production environments. © 2025 IEEE.

关键词： Smart manufacturing

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Hierarchical Bayesian Inference and Advanced Multivariate Computational Paradigms for Precision-Enhanced Personalization in Multi-Entity Culinary Ordering systems 3

Hierarchical Bayesian Inference and Advanced Multivariate Co...

引用

3rd international conference for Advancement in Technology, ICONAT 2024

作者： Shaikh, Anis Maurya, Omkar Bhagat, Aditya Jachak, Sanika Raipure, Shwetal JSPM's Rajarshi Shahu College of Engineering Department of Computer Engineering Maharashtra Pune411033 India

ISBN: (纸本)9798350354171

machine learning (ML) technology is growing very fast and could potentially revolutionize service-oriented systems, especially in food pre-ordering. This paper presents the results of a survey conducted regarding technological advances and trends for canteen food-ordering systems using machine learning techniques in a way that helps improve and personalize the ordering experience from multiple canteens. The ML models leverage different functions to manage preferences of users, having considered their purchase and transaction history, in order to recommend food to them. Some of the important features are a recommendation system based on individual tastes, order forecasting processes via predictive analysis, and a vendor dashboard for data management. This paper evaluates the impact of implementation of ML on operational efficiency, customer satisfaction, a quicker and safer payment method and real-time data extraction technique. Conclusively, the research findings are that the quality of service can be significantly upgraded while reducing the wait times and making the ordering experience very personalized. At the same time, the downsides of zero-input technology will also be taken into account. © 2024 IEEE.

关键词： Customer satisfaction

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AI Driven Soil Monitoring and Crop Recommendation using machine learning Algorithm 15

AI Driven Soil Monitoring and Crop Recommendation using Mach...

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15th international conference on advances in Computing, Control, and Telecommunication Technologies, ACT 2024

作者： Shenbagavadivu, S. Susee, S.K. Chidhambarajan, B. Kumar, Senthil M. Department of Information Technology SRM Valliammai Engineering College Kattankulathur Tamil Nadu Chengalpattu District India Department of Electronics and Communication Engineering SRM Madurai College for Engineering and Technology Tamil Nadu Madurai India SRM Valliammai Engineering College Kattankulathur Tamil Nādu Chengalpattu District India Department of Cyber Security SRM Valliammai Engineering College Kattankulathur Tamil Nadu Chengalpattu District India

ISBN: (纸本)9798331300579

Farmers, notably in India, face challenges such as inadequate expertise in crop selection and crop failures due to *** learning's untapped potential in agriculture, limited by data quality and processing constraints, presents an opportunity for *** objective is to give an open-source online application is developed using Flask to aid farmers in informed *** Precision Agriculture principles to recommend crops based on soil types, features, and data-driven insights to boost *** various methods based on a machine learning-based ensemble model is employed to suggest suitable crops based on soil data, focusing on accuracy and *** outcome of the developed system shows promise in enhancing input-output efficiency, improving decision-making, and reducing unsuitable crop choices, thereby boosting agricultural productivity and positively impacting India's *** approach demonstrates practical potential in resolving farming challenges by leveraging machine learning for crop recommendations, potentially revolutionizing agriculture in India and beyond. © Grenze Scientific Society, 2024.

关键词： Adversarial machine learning

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AI-Driven Real-Time Incident Detection for Intelligent Transportation systems 7

AI-Driven Real-Time Incident Detection for Intelligent Trans...

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international conference on Intelligent Traffic and Transportation (ICITT)

作者： Gkioka, Georgia Dominguez, Monica Tympakianaki, Athina Mentzas, Gregoris Natl Tech Univ Athens NTUA Informat Management Unit Inst Commun & Comp Syst ICCS Patission 42 Athens 10682 Greece

ISBN: (纸本)9781643685052;9781643685045

Efficient automatic detection of incidents is a well-known problem in the field of transportation. Non-recurring incidents, such as traffic accidents, car breakdowns, and unusual congestion, can have a significant impact on journey times, safety, and the environment, leading to socio-economic consequences. To detect these traffic incidents, we propose a framework that leverages big data in transportation and data-driven Artificial Intelligence (AI)-based approaches. This paper presents the proposed methodology, conceptual and technical architecture in addition to the current implementation. Moreover, a comparison of data-driven approaches is presented, the findings from experiments to explore the task using real-world datasets are examined, while highlighting limitations of our work and identified challenges in the mobility sector and finally suggesting future directions.

关键词： incident detection artificial intelligence big data machine learning deep learning intelligent transportation system.

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Detection of DDoS Attack: A Comparative Evaluation of machine learning Techniques

Detection of DDoS Attack: A Comparative Evaluation of Machin...

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2024 IEEE international conference on Signal Processing, Information, Communication and systems, SPICSCON 2024

作者： Mimi, Afsana Moon, Iffath Tanjim Mridha, Md Musfiqur Rahman Jafor, Md Shakiul Department of Computer Science & Engineering Bangladesh Department of Electrical and Electronic Engineering Bangladesh Pundra University of Science & Technology Bogura Bangladesh Varendra University Rajshahi Bangladesh

ISBN: (纸本)9798331510213

Distributed denial-of-service (DDoS) attacks are a common and increasing threat to online services, targeting multiple layers of the OSI model. As communication technology advances, these attacks are getting more frequent and complicated, making detection and protection more challenging. Therefore, the need to study DDoS attack detection is growing. This paper evaluated multiple machine learning models for detecting DDoS attacks using binary classifications. We have used nine machine learning models for binary classification namely Support Vector machine, Random Forest, Gaussian Naïve Bayes, Extra Trees, AdaBoost, XGBoost, Gradient Boosting, Logistic Regression, and Multilayer Perceptron. Besides, we have selected the KDDCup99 dataset for this research. The dimensions of data determine a detection system's usefulness, consequently, feature selection and preprocessing are used to reduce the dataset's dimensionality, which minimizes the mathematical complexity. The evaluation of models is based on the accuracy, recall, precision, f1-score, false alarm rate, false positive ratio, false negative ratio, and error rate of datasets. Finally, using these evaluation criteria, we have compared the performance of the machine learning models and achieved the best results in binary classification using the XGBoost model with the highest accuracy (99.98%), precision (99.97%), recall (99.96%), f1-score (99.97%), and lowest false alarm rate (0.01%), error rate (0.02%), false positive ratio (0.03%), and false negative ratio (0.03%). © 2024 IEEE.

关键词： Contrastive learning

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Chronic Kidney Disease Prediction Using machine learning Algorithms

Chronic Kidney Disease Prediction Using Machine Learning Alg...

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2024 international conference on advances in Modern Age Technologies for Health and engineering Science, AMATHE 2024

作者： Nagamani, T. Kavya, R. Lavanya, D. Miruthula, S. Kongu Engineering College Department Of Computer Science And Engineering Tamil Nadu Erode India

ISBN: (纸本)9798350371567

Early detection and characterisation play a crucial role in effectively managing and treating chronic illness. Given the increasing prevalence of chronic kidney disease (CKD), the healthcare system faces a significant burden, as CKD raises the risk of developing end-stage renal disease and related morbidity and mortality. The timely identification of CKD is vital to saving numerous lives. This study uses a feature selection strategy alongside various machine learning (ML) algorithms to develop a diagnostic system for detecting CKD. The study utilised approximately 400 clinical records of CKD patients sourced from the UCI machine learning repository. To refine the datasets for the predictive model, various data preparation techniques were applied, including feature encoding, label encoding, data cleaning, ambiguity removal, handling missing values, and feature selection. Ultimately, the Extra Trees classifier outperformed the other two machine learning models, exhibiting the capability to diagnose chronic kidney disease (CKD) with an impressive 98% accuracy rate and a low 2% true negative rate, all without any data leakage. © 2024 IEEE.

关键词： Decision trees

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Advancements in Object Detection for Accurate and Efficient Visual Recognition Using machine learning 2

Advancements in Object Detection for Accurate and Efficient ...

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2nd IEEE international conference on advances in Information Technology, ICAIT 2024

作者： Fernandez, F. Mary Harin Ramachandran, A. Ganesh Saravanan, S.K. Bhanumathi, M. Sangeetha, M. Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai India Department of Computer Science Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai India Department of Computers Science and Engineering BigData and Network Security Saveetha School of Engineering SIMATS Tamil Nadu Chennai India Chennai India Computer Engineering Department University of Technology and Applied Sciences Nizwa Oman

ISBN: (纸本)9798350383867

Object detection in images and videos is a vital task in computer vision where its application goes beyond the automobile driving and surveillance, to healthcare. The paper introduces an object detection model which is specific and powerful, that is able to recognize visually with machine learning. The framework is constructed on the basis of the recent innovations in deep learning and attentive mechanisms, which not only lead to the best performance in object detection today but also guarantee the widest applicability. Using an extensive experiment which reveals the described model superiority over the current state-of-the-art algorithms by evaluating mAP indices, whereas the latter has a normal complexity. The ablation study uncovers the impact of core parameters including attention mechanisms and semi-supervised techniques, underlining their importance in the growth of detection precision. Moreover, the illustrative comparison of outcomes from detecting objects on the framework calls for attention to the capacity of the networks to objectify in different categories. Our outcome also suggest that although the proposed framework has a human-level performance in task of object detection there is still a scope to enhance their performance above the human level. Summarizing, this work concludes the conversation and can be considered in the progress of research developments of the object detection systems aimed at practical object meeting situations. © 2024 IEEE.

关键词： Computer Vision Deep learning machine learning Object Detection Visual Recognition

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Detecting and Mitigating Data Integrity Attacks on Distributed Algorithms for Optimal Power Flow using machine learning 57

Detecting and Mitigating Data Integrity Attacks on Distribut...

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57th Hawaii international conference on System Sciences (HICSS)

作者： Harris, Rachel Molzahn, Daniel K. Georgia Inst Technol Atlanta GA 30332 USA

ISBN: (纸本)9780998133171

Using distributed algorithms, multiple computing agents can coordinate their operations by jointly solving optimal power flow problems. However, cyberattacks on the data communicated among agents may maliciously alter the behavior of a distributed algorithm. To improve cybersecurity, this paper proposes a machine learning method for detecting and mitigating data integrity attacks on distributed algorithms for solving optimal power flow problems. In an offline stage with trustworthy data, agents train and share machine learning models of their local subproblems. During online execution, each agent uses the trained models from neighboring agents to detect cyberattacks using a reputation system and then mitigate their impacts. Numerical results show that this method reliably, accurately, and quickly detects data integrity attacks and effectively mitigates their impacts to achieve near-feasible and near-optimal operating points.

关键词： Cybersecurity Distributed Optimization Optimal Power Flow Data Integrity Attack

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Actual Rate Prediction: Extension in Real Estate Prediction using machine learning 1

Actual Rate Prediction: Extension in Real Estate Prediction ...

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1st IEEE international conference on advances in Computing, Communication and Networking, ICAC2N 2024

作者： Archana, Archana Bhatnagar, Prachi Singh, Narendra Sharma, Manoj Kumar Kumar, Gaurav Pandey, Mahima Shankar Bansal, Nidhi IIMT University Department of Computer Science and Engineering Uttar Pradesh Meerut India IIMT University Department of Computer Science and Application Uttar Pradesh Meerut India Panchwati Institute of Engineering and Technology Department of Computer Science and Engineering Uttar Pradesh Meerut India Vishveshwarya Group of Institute Dadri Department of Computer Science and Engineering Uttar Pradesh GB Nagar India Galgotia College of Engineering and Technology Department of Computer Science and Engineering Uttar Pradesh Greater Noida India Symbiosis Institute of Technology Nagpur Campus Pune India

ISBN: (纸本)9798350356816

machine learning algorithms always perform well to solve real-life problems encountered in daily life. Due to large data sets, analysis is also becoming too complex to predict anything. A lot of calculations are being done to find the exact patterns and values to get accurate results. Here machine learning algorithms are giving tremendous performance while applying various classification techniques as it gives algorithms based on specifications for different aspects. In different cities, forecasting is necessary as people must move from one place to another due to the pandemic situation. A case study has also been conducted with real estate real data by applying machine learning algorithms. A new model has also been implemented with Jupyter Notebook in the proposed paper and has received some positive feedback. © 2024 IEEE.

关键词： Adversarial machine learning

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