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Abuse Reporting System 6

Abuse Reporting System

引用

6th IEEE international conference on Recent advances in Intelligent Computational systems, RAICS 2024

作者： Sivadasan, Sreeram Mohamed Jisar, C. Rasmina Murshida Farsana, V. Shijaz, K. Mea Engineering College Department of Computer Science and Engineering India

ISBN: (数字)9798350381689

ISBN: (纸本)9798350381689

This project embarks on the development of an abuse reporting system designed to maintain user anonymity and employ advanced machine learning algorithms for genuineness assessment and fraud detection. In the contemporary digital landscape, safeguarding user privacy and ensuring the reliability of abuse reports are paramount. Our project addresses this challenge by integrating state-of-the-art machine learning techniques, enhancing the accuracy and efficiency of abuse reporting process, and mitigating false or malicious reporting. The technical underpinnings of the system involve the creation of a distributed, scalable, and fault-tolerant architecture capable of handling a wide range of abuse reports from diverse sources. We implement advanced natural language processing (NLP) models for textbased report analysis, while image and multimedia reports undergo deep learning-based content analysis. Anonymity preser-vation is achieved through advanced encryption techniques and decentralized data storage. Furthermore, we integrate adaptive machine learning algorithms that continuously evolve to detect novel abuse patterns and emerging fraud strategies, ensuring the system's adaptability to evolving threats. The project includes a comprehensive evaluation of the abuse reporting app's performance, precision, and scalability in handling a large volume of reports while preserving user anonymity. This project represents a significant advancement in the field of abuse reporting systems, offering a technologically robust and privacy-centric solution for contemporary society's increasing demand for secure and effective reporting mechanisms. © 2024 IEEE.

关键词： Anonymity

来源：评论

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machine learning for Modeling Service Life: Comprehensive Review, Bibliometrics Analysis and Taxonomy 27

Machine Learning for Modeling Service Life: Comprehensive Re...

引用

27th IEEE international conference on Intelligent engineering systems, INES 2023

作者： Mudabbir, Mohammed Mosavi, Amirhosein Banki Donat Faculty of Mechanical and Safety Engineering Budapest Hungary John von Neumann Faculty of Informatics Budapest Hungary University of Public Service Budapest Hungary

ISBN: (纸本)9798350328516

This article presents a comprehensive review of the machine learning methods used to model the service life of various products, which is a critical aspect of product development and production. With the recent advances in machine learning, it has become increasingly feasible to utilize these methods for modeling service life accurately. This review provides a detailed examination of the existing literature on machine learning applications for modeling service life, including a bibliometric analysis of the most frequently cited works. Furthermore, this review presents a taxonomy of the various machine learning methods employed in service life modeling, highlighting the fundamental methods such as Artificial neural networks, Support vector machine, and decision trees. The results of this review demonstrate the potential of machine learning methods for accurately modeling service life, while also emphasizing the need for further research in this field. Overall, this article provides a valuable resource for researchers and practitioners looking to apply machine learning methods for modeling service life. © 2023 IEEE.

关键词： Support vector machines

来源：评论

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Immersive Recommendations: Influencers Recommendations Using machine learning 1

Immersive Recommendations: Influencers Recommendations Using...

引用

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

作者： Alavala, Hemanth Reddy Karlapalam, Jaithra Sarma Jain, Manish Basavaraju, Sagar School of Engineering Bengaluru Amrita Vishwa Department of Electronics and Communication Engineering Amrita Vidyapeetham India

ISBN: (纸本)9798331518578

The rapid growth of social media has made influencer marketing a vital strategy for brands to engage with their target audience. Identifying the right influencers who can effectively promote products and drive engagement is crucial for the success of marketing campaigns. This paper focuses on comparison of machine learning models used for recommending social media influencers, specifically analyzing using YouTube channel data to predict influencer effectiveness. While previous studies have explored collaborative filtering and content-based approaches, there is a gap in integrating diverse machine learning models, particularly in terms of improving prediction accuracy and adapting to dynamic audience behavior. The challenges of selecting the optimal model arise from the variability in influencer metrics and the need for robust predictions in an ever- changing social media landscape. In this work, we propose a novel methodology that applies 20 different machine learning models, including deep neural networks, to comprehensively analyze influencer data. Our evaluation demonstrates that models like Hybrid Gradient Boosting, Random Forest, and Recurrent Neural Networks offer significantly higher performance and robustness compared to traditional methods, providing an adaptable and reliable recommendation system for influencer effectiveness. © 2025 IEEE.

关键词： Adversarial machine learning

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Revolutionizing Dementia Diagnosis: Integrating DenseNet121 and SVM for Early Detection through MRI Analysis 3

Revolutionizing Dementia Diagnosis: Integrating DenseNet121 ...

引用

international conference on advances in Computing, Communication and Applied Informatics (ACCAI)

作者： Vishwanadham, Y. K. Balaji, Nuthalapati Alla, Vaishnavi Gopi, Rajulapati Venkata Sai Satya, Posina Manideep Dheeraj, Palaparthi Seshadri Rao Gudlavalleru Engn Coll Dept Informat Technol Vijayawada Andhra Pradesh India

ISBN: (纸本)9798350389432;9798350389449;9798350389456

Dementia, a debilitating condition affecting cognitive functions, necessitates early detection for effective management. This research introduces an innovative approach to diagnosed ementia by employing machine learning techniques, specifically integrating the Dense Net121 feature extractor and a Support Vector machine (SVM) *** a diverse MRI dataset obtained from Kaggle, comprising various stages of dementia, the model demonstrates promising results interms of precision and accuracy. Overcoming limitations of existing systems, the proposed method addresses temporal modeling challenges, imbalanced datasets, and high dimensionality associatedwith MRI data. The Flask web application developed facilitates user-friendly interaction for healthcare professionals, offering a practical tool for efficient dementia diagnosis. The study underscores the significance of early detection in influencing potential treatment strategies and contributes to the broader field of medical *** findings highlight the effectiveness of DenseNet121 and SVM integration, show casing the potential of machine learning in conjunction with medical imaging for earlydementiadetection.

关键词： Dementia DenseNet121 Support Vector machine machine learning EarlyDetection

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Introducing CausalBench: A Flexible Benchmark Framework for Causal Analysis and machine learning 24

Introducing CausalBench: A Flexible Benchmark Framework for ...

引用

33rd ACM international conference on Information and Knowledge Management (CIKM)

作者： Kapkic, Ahmet Mandal, Pratanu Wan, Shu Sheth, Paras Gorantla, Abhinav Choi, Yoonhyuk Liu, Huan Candan, K. Selcuk Arizona State Univ Tempe AZ 85287 USA

ISBN: (纸本)9798400704369

While witnessing the exceptional success of machine learning (ML) technologies in many applications, users are starting to notice a critical shortcoming of ML: correlation is a poor substitute for causation. The conventional way to discover causal relationships is to use randomized controlled experiments (RCT);in many situations, however, these are impractical or sometimes unethical. Causal learning from observational data offers a promising alternative. While being relatively recent, causal learning aims to go far beyond conventional machine learning, yet several major challenges remain. Unfortunately, advances are hampered due to the lack of unified benchmark datasets, algorithms, metrics, and evaluation service interfaces for causal learning. In this paper, we introduce Causal-Bench, a transparent, fair, and easy-to-use evaluation platform, aiming to (a) enable the advancement of research in causal learning by facilitating scientific collaboration in novel algorithms, datasets, and metrics and (b) promote scientific objectivity, reproducibility, fairness, and awareness of bias in causal learning research. Causal-Bench provides services for benchmarking data, algorithms, models, and metrics, impacting the needs of a broad of scientific and engineering disciplines.

关键词： Benchmark Causality machine learning Dataset Model Metric

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Intrusive Detection in Nuclear systems using Supervised and Unsupervised M1 Methodologies 5

Intrusive Detection in Nuclear Systems using Supervised and ...

引用

5th IEEE international conference on advances in Electronics, Computers and Communications, ICAECC 2023

作者： Subashree, V. Jessica, J. Hemalatha, R. Rishika, S. Saveetha Engineering College Department of Electronics and Communication Engineering Chennai India

ISBN: (纸本)9798350305449

In order to safeguard vital infrastructure, nuclear systems must employ intrusion detection. In such a dynamic environment, it may be challenging for conventional intrusion detection methods to identify modern, adaptive attacks. Utilizing machine learning to enhance nuclear intrusion detection is an emergent field of study. This study provides an overview of machine learning methods, including examples of their application, for nuclear facility intrusion detection. Both unsupervised learning strategy anomaly detection (Isolation Forest), and supervised learning algorithm Convolutional Neural Networks. This conducts a series of case studies using data from operational nuclear systems to evaluate the efficacy of these machine learning methods. The present experimental data, evaluate the efficacy of different algorithms, and discuss to select and preprocess features. The study also discusses the challenges and potential solutions associated with the use of machine learning for the detection of nuclear system intrusions. Both models perform well in detecting intrusions, with CNN outperforming the baseline and Isolation Forest in terms of performance metrics. This demonstrates how beneficial CNN is for enhancing the intrusion detection of nuclear systems by identifying subtle patterns and features in vast quantities of data. Our findings shed light on the potential of machine learning to improve the security of nuclear systems and provide valuable lessons for future cyber security professionals. © 2023 IEEE.

关键词： Anomaly detection

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Voice Based Stress Analysis and Detection Using machine learning 10

Voice Based Stress Analysis and Detection Using Machine Lear...

引用

10th international conference on Advanced Computing and Communication systems, ICACCS 2024

作者： Kanisha, S. Kumar, E. Nikhil Charitha, N. Mehda, B. Vishnu, A.S. Tilak, Raghu New Horizon College of Engineering Department of Ce Bangalore India New Horizon College of Engineering Department of Me Bangalore India

ISBN: (纸本)9798350384369

With a major focus on monitoring depression, this research presents a novel machine learning approach for voice-based stress analysis and detection. Our technique uses advances in artificial intelligence to anticipate the Beck Depression Inventory II (BDI-II) scores based just on speech patterns. We use a multi- pronged approach to accomplish this, which involves extracting mel-frequency cepstral coefficients and spectral low-level descriptors from brief audio clips. These characteristics pick up on subtleties in voice expressions that could point to underlying depression or stress. Additionally, in order to improve our model's capacity to identify patterns linked to depression, we suggest using a feature dynamic history histogram (FDHH) to record temporal changes in the feature space. Regression approaches are used to fuse FDHH and audio features, providing a thorough study of vocal data for accurate BDI-II scale prediction. Our approach, which just looks at voice cues, provides a non-invasive and effective way to analyze stress and identify depression. This approach may find use in mental health monitoring and treatment. © 2024 IEEE.

关键词： machine learning

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Employee Retention Analysis: Insights and Predictive Modeling With machine learning Techniques 1

Employee Retention Analysis: Insights and Predictive Modelin...

引用

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

作者： Prajapati, Jyoti Vetrivendan, L. Mishra, Kanderp Narayan Radhakrishnan, R. Ranjith, M. School of Computer Science and Engineering Galgotias University Greater Noida India Alliance University India Sharda University India School of Computer Science Galgotias University Greater Noida India

ISBN: (纸本)9798331518578

Brain tumors are one of the most serious and potentially fatal diseases in the world, to have any hope of being successfully treated it is imperative that they be discovered early. Conventional diagnostic methods such as MRI and CT images often rely on manual interpretation;this can take a long time and is prone to human error. To address this situation, we present a deep learning-based approach which uses a Deep Convolutional Neural Network for automatic detection of brain tumors. The resultant model takes advantage of the powerful feature extraction abilities of CNNs in order to divide brain images into tumor and non-tumor categories with high precision. The dataset consists of MRI scans and has been preprocessed to enhance image quality while removing noise. Employing advanced techniques such as data augmentation, transfer learning and hyperparameter tuning, the structure of this CNN network for brain tumor detection is optimized. Experimental results prove that the combination is superior to traditional machine learning methods. In medical imaging, this research shows that the potential for deep learning is enormous and will help pave the way for easier, accurate brain tumor detection systems. © 2025 IEEE.

关键词： Convolutional neural networks

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An Integrated Development Of IoT Based machine learning For Data-Driven Travel Recommendations

An Integrated Development Of IoT Based Machine Learning For ...

引用

2024 international conference on advances in Data engineering and Intelligent Computing systems, ADICS 2024

作者： Rifath, Shakeel Sridhar Xavier, Constance Brindasri, S. Hindustan Institute Of Technology And Science Computer Science And Engineering Department Chennai India

ISBN: (纸本)9798350364828

An innovative methodology is introduced in this Research to enhance data-driven travel decision-making by integrating IoT technology and data science techniques. With a specific focus on air and sound pollution, the research utilizes real-time sensor data from various urban sources to develop predictive models for forecasting future pollution levels. These models serve as the backbone for decision support tools seamlessly integrated into open-source platforms like ThingSpeak and IFTTT, facilitating proactive travel planning for both the general public and patients. The core of this methodology lies in the real-time aggregation of IoT sensor data, offering users comprehensive insights into prevailing air pollution levels at their intended destinations. Augmented by predictive machine learning algorithms such as logistic regression and decision trees, the system's real-time monitoring capabilities are significantly enhanced, with the decision tree algorithm particularly noted for its accuracy in assessing destination safety for patients. The forecasting accuracy and reliability of the model are upheld through rigorous training on historical data. Notably, the synergy between ThingSpeak and IFTTT enables the delivery of personalized travel recommendations grounded in predictive analytics and real-time environmental monitoring. This research underscores the pivotal role of data-driven decision-making in travel planning, empowering patients with tailored guidance derived from robust data analysis. © 2024 IEEE.

关键词： Predictive analytics

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Evolutionary Computation-Based Scheduling of machine learning Workloads for GPU Clusters 5

Evolutionary Computation-Based Scheduling of Machine Learnin...

引用

5th international conference on advances in Electrical engineering and Computer Applications, AEECA 2024

作者： Kwon, Seokmin Bahn, Hyokyung Ewha University Dept. of Computer Engineering Seoul Korea Republic of

ISBN: (纸本)9798350355253

Recently, machine learning (ML) workloads across diverse industries such as smart logistics, finance, and entertainment are increasingly being executed on cloud platforms. Efficient scheduling of these ML workloads is challenging as various types of workloads coexist and the cluster systems feature heterogeneous GPU resources. Although task scheduling has been extensively studied, traditional scheduling policies do not perform well in such environments as they cause resource fragmentation problems, which significantly lowers GPU utilization. To address this issue, this paper proposes a new scheduling approach utilizing evolutionary computation techniques, and implements it within a process-based event simulation framework. Experimental results, replicating extensive ML task traces collected from Alibaba's MLaaS cluster, demonstrate that the proposed scheduling approach significantly improves GPU utilization compared to conventional scheduling policies. It is anticipated that the scheduling policies proposed in this paper will be used effectively for the resource allocation of ML workloads in future GPU cluster systems. © 2024 IEEE.

关键词： Cloud platforms

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