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2nd International Conference on Machine learning and Autonomous Systems, ICMLAS 2025

作者： Jikar, Nayan Tale, Yash Tale, Abhay Barhate, Aditya Verma, Prateek Jikar, Aman Datta Meghe Institute of Higher Education and Research Sawangi Faculty of Engineering and Technology Department of Artificial Intelligence and Machine Learning Maharashtra Wardha442001 India Datta Meghe Institute of Higher Education and Research Sawangi Faculty of Engineering and Technology Department of Artificial Intelligence and Data Science Maharashtra Wardha442001 India Datta Meghe Institute of Higher Education and Research Faculty of Engineering and Technology Department of Computer Science and Engineering Maharashtra Wardha442001 India

ISBN: (纸本)9798331505745

The huge volumes of data produced in social media provide both new possibilities and challenges to analytics. The present paper emphasizes Big data analytics and Machine learning (ML) methods to uncover trends, patterns, and user behavior from social media-related data. Such vital concepts, like the 'Four Vs' of Big data: Volume, Velocity, Variety, and Veracity, and innovative tools for datacollection, storage, and preprocessing are covered here as well. Sentiment analysis, trend forecasting, and user behaviormodeling are some of the examples of what is possible using ML and Artificial Intelligence (AI) for deriving actionable insights. Marketing campaigns, crisis communications, and political campaigns are some of the scenarios that are covered to bring out real world applications of such technologies, along with their transformative effects. Lastly, data privacy, quality, and scalability issues are noted, along with ethical concerns and biases. Directions for the future point to developments in real-time data analysis, anonymization, and the changing role of AI in social media platforms. This research emphasizes the significance of Big data and machine learning in driving innovation and decision-making in the digital era. © 2025 IEEE.

关键词： Tweets

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Mixing Histopathology Prototypes into Robust Slide-Level Representations for Cancer Subtyping

arXiv

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arXiv 2023年

作者： Butke, Joshua Hashimoto, Noriaki Takeuchi, Ichiro Miyoshi, Hiroaki Ohshima, Koichi Sakuma, Jun Machine Learning and Data Mining Lab University of Tsukuba Japan RIKEN Center for Advanced Intelligence Project Japan Department of Mechanical Systems Engineering Nagoya University Japan Department of Pathology Kurume University Japan Department of Computer Science Tokyo Institute of Technology Japan

Whole-slide image analysis via the means of computational pathology often relies on processing tessellated gigapixel images with only slide-level labels available. Applying multiple instance learning-based methods or transformer models is computationally expensive as, for each image, all instances have to be processed simultaneously. The MLP-Mixer is an under-explored alternative model to common vision transformers, especially for large-scale datasets. Due to the lack of a self-attention mechanism, they have linear computational complexity to the number of input patches but achieve comparable performance on natural image datasets. We propose a combination of feature embedding and clustering to preprocess the full whole-slide image into a reduced prototype representation which can then serve as input to a suitable MLP-Mixer architecture. Our experiments on two public benchmarks and one inhouse malignant lymphoma dataset show comparable performance to current state-of-the-art methods, while achieving lower training costs in terms of computational time and memory load. Code is publicly available at https://***/butkej/ProtoMixer. Copyright © 2023, The Authors. All rights reserved.

关键词： Mixers (machinery)

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Intrusion Detection in Cloud Architecture Using Machine learning 4

Intrusion Detection in Cloud Architecture Using Machine Lear...

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4th International Conference on Advances in Computing, Communication Control and Networking, ICAC3N 2022

作者： Govinda Rajulu, G. Santhoshkumar, R. Venkatesan, D. Kalvikkarasi, S. Santosh Kumar Patra, P. St. Martin's Engineering College Department of Computer Science and Design India St. Martin's Engineering College Department of Artificial Intelligence and Data Science India St. Martin's Engineering College Department of Artificial Intelligence and Machine Learning Department of CSE India Podhagai College of Engineering & Technology Professor in Ece Department India St. Martin's Engineering College India

ISBN: (纸本)9781665474368

The research of Intrusion Detection Systems is known to be among the most challenging aspects of Cloud Security. In the context of the cloud, security breaches such as denial of service, scanning, injection of malicious code, viruses, worms, and the breaking of passwords are becoming increasingly commonplace. In the event that these attacks are not stopped in a timely manner, the company's reputation may be put in jeopardy, and it may sustain financial harm. As a consequence of this, it is essential to safeguard the cloud against dangers of this kind. In this research, we provide solutions to defend cloud systems by incorporating some of the most successful methods of intrusion detection. Within IDS, our primary focus has been placed on two critical aspects: the effectiveness of the detection techniques and the pace of detection. In order to overcome these obstacles, we suggest utilising machine learning in conjunction with parallelization. Approaches based on machine learning gain information from the data itself, hence diminishing the significance of the human expert. In order to identify intrusions, statistical models, safe system approaches, neural networks, and a variety of other methods are utilised. The purpose of this study is to improve the accuracy of intrusion detection in cloud computing by presenting a new approach to intrusion detection that is based on existing intrusion detection systems and the various architectures that those systems employ. © 2022 IEEE.

关键词： Cloud computing architecture

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A Review on the Developments in the Field of AI-Based Gait Analysis

A Review on the Developments in the Field of AI-Based Gait A...

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Artificial Intelligence in Education and Industry 4.0 (IDICAIEI), DMIHER International Conference on

作者： Vidhi Wankhede Prateek Verma Shailesh Gahane Department of Artificial Intelligence and Data Science Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research (DU) Wardha Maharashtra India Department of Artificial Intelligence and Machine Learning Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research (DU) Wardha Maharashtra India Faculty of Sciences and Technology Datta Meghe Institute of Higher Education and Research Wardha Maharashtra India

ISBN: (数字)9798331528713

ISBN: (纸本)9798331528720

The examination of human gait is essential for comprehending motor function, the evolution of diseases, and the effectiveness of rehabilitation in different clinical and research environments. Artificial Intelligence (AI) advancements have recently improved gait analysis methodologies, resulting in increased accuracy, efficiency, and therapeutic effectiveness. This review article compiles the existing literature on AI-based gait analysis. The review emphasizes the role of AI in gait analysis, namely via the use of vision-based approaches, wearable sensor technologies, markerless motion capture systems, and inertial sensor-based methodologies. AI has shown potential in identifying musculoskeletal problems including osteopenia and sarcopenia, forecasting the start of Parkinson's disease, assessing the effects of post-stroke rehabilitation, and diagnosing neurological conditions. In addition, the integration of AI with new technologies such as augmented reality (AR), smart sensors, and wearable devices presents intriguing opportunities to improve gait rehabilitation, enable individualized therapies, and facilitate remote patient monitoring. Although there have been significant breakthroughs, there are still obstacles in creating AI models that can be easily understood for clinical decision-making, establishing standardized procedures for collecting data, and verifying AI algorithms across various populations. This study underlines the transformational potential of AI-based gait analysis in customized healthcare, rehabilitation techniques, and clinical results. In order to fully harness the potential of AI in enhancing human health and well-being, it is imperative to do more research and foster multidisciplinary cooperation.

关键词： Patient monitoring Musculoskeletal system Reviews Parkinson's disease Medical treatment Motion capture Artificial intelligence Wearable devices Gait recognition Wearable sensors

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FairTP: A Prolonged Fairness Framework for Traffic Prediction

arXiv

引用

arXiv 2024年

作者： Xia, Jiangnan Yang, Yu Shen, Jiaxing Wang, Senzhang Cao, Jiannong School of Computer Science and Engineering Central South University China Centre for Learning Teaching and Technology The Education University of Hong Kong Hong Kong School of Data Science Lingnan University Hong Kong The Department of Computing The Hong Kong Polytechnic University Hong Kong

Traffic prediction is pivotal in intelligent transportation systems. Existing works mainly focus on improving the overall accuracy, overlooking a crucial problem of whether prediction results will lead to biased decisions by transportation authorities. In practice, the uneven deployment of traffic sensors in different urban areas produces imbalanced data, making the traffic prediction model fail in some areas and leading to unfair regional decision-making that eventually severely affects equity and quality of residents' life. Additionally, existing fairness machine learning models fail to preserve fair traffic prediction for a prolonged time. Although they can achieve fairness at certain time points, such static fairness will be broken as the traffic conditions change. To fill this research gap, we investigate prolonged fair traffic prediction, introduce two novel fairness definitions tailored to dynamic traffic scenarios, and propose a prolonged fairness traffic prediction framework, namely FairTP. We argue that fairness in traffic scenarios changes dynamically over time and across areas. Each traffic sensor or city area has state that alternates between "sacrifice" and "benefit" based on its prediction accuracy (high accuracy indicates "benefit" state). Prolonged fairness is achieved when the overall states of sensors similar within a given ***, we first define region-based static fairness and sensor-based dynamic fairness. Next, we designed a state identification module in FairTP to discriminate between states of "sacrifice" or "benefit" to enable prolonged fairness-aware traffic predictions. Lastly, a state-guided balanced sampling strategy is designed to select training examples to promote prediction fairness further, mitigating the performance disparities among regions with imbalanced traffic sensors. Extensive experiments in two real-world datasets show that FairTP significantly improves prediction fairness without causing much accuracy degrada

关键词： Intelligent systems

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Condition Monitoring and Fault Diagnosis of BLDC Motor in Electric Vehicles Using Artificial Intelligence

Condition Monitoring and Fault Diagnosis of BLDC Motor in El...

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Artificial Intelligence in Education and Industry 4.0 (IDICAIEI), DMIHER International Conference on

作者： Swapnil Gundewar Meher Langote Swapna Kamble Prashant Kamble Artificial Intelligence and Machine Learning Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research Sawangi Maharashtra India Artificial Intelligence and Data Science Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research Sawangi Maharashtra India Department of Information Technology Yeshwantrao Chavan College of Engineering Nagpur Maharashtra India Department of Mechanical Technology Yeshwantrao Chavan College of Engineering Nagpur Maharashtra India

ISBN: (数字)9798331528713

ISBN: (纸本)9798331528720

Due to their high benefits in terms of environment and standards of battery advancement, Electric Vehicles (EVs) are extremely important in the shift towards transportation. Here the Brushless DC (BLDC) motor is an integrated part of the EVs, and it has become the most popular choice due to better efficiency, reliability, and reduced maintenance requirements than the others. The major difference from traditional motors is that BLDC motors make use of electronic commutation, so brushes are not employed; this results in lower amounts of wear. This not only helps the motor to have a longer working life but also to work more efficiently, or to maintain their efficiency. However, one crucial drawback to using BLDC motors is the fact that they tend to perform and have great durability if the condition monitoring and fault diagnosis systems when utilized top-notch. These systems are important for the identification of abnormalities and possible defects that if not attended to can lead to major failures and long periods of non-productive time. The ability to track the state of BLDC motors in real time enables one to have preventive measures that cut the chances of the motor breaking down and consequently the costs attached to this eventuality. Condition monitoring involves continuous assessment of motor attributes such as; vibration, temperature, and electrical patterns in a bid to detect changes in healthy motor status. Whereas the existing literature on the level of faults goes beyond it by identifying the specific nature and location of the faults. Altogether, the procedures composed play the basic role of a predictive maintenance program and check BLDC motors’ performance and reliability throughout their usage. Given the importance of BLDC motors in EVs, improvements in the reliability of condition monitoring and fault diagnosis are unpreventable for the enhancement and sustainability of electric mobility.

关键词： Fault diagnosis Temperature measurement Condition monitoring Temperature sensors Vibrations Machine learning Motors Electric vehicles Maintenance Monitoring

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A Review on Bayesian Methods for Uncertainty Quantification in Machine learning Models Enhancing Predictive Accuracy and Model Interpretability

A Review on Bayesian Methods for Uncertainty Quantification ...

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Intelligent Cyber Physical Systems and Internet of Things (ICoICI), International Conference on

作者： Moses Makuei Jiet Prateek Verma Aahash Kamble Chetan Puri Department of Computer Science and Design Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research Sawangi Wardha Maharashtra India Department of Artificial Intelligence and Machine Learning Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research Sawangi Wardha Maharashtra India Department of Artificial Intelligence and Data Science Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research Sawangi Wardha Maharashtra India

ISBN: (数字)9798331540661

ISBN: (纸本)9798331540678

This research study reviews the statistical fundamentals of machine learning with a focus on Bayesian methods to quantify the uncertainty in model predictions. Bayesian statistics provides a framework for incorporating prior knowledge, updating beliefs, and expressing uncertainty in predictions. This research study will explore Bayesian techniques applied to various aspects of machine learning, including regression, classification, deep learning, and ensemble methods.

关键词： Uncertainty Accuracy Reviews Computational modeling Pipelines Predictive models Probabilistic logic Bayes methods Internet of Things Ensemble learning

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A Review on Soil Moisture Monitoring Methods using Satellite Images

A Review on Soil Moisture Monitoring Methods using Satellite...

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Artificial Intelligence in Education and Industry 4.0 (IDICAIEI), DMIHER International Conference on

作者： Prateek Verma Aahash Kamble Aditya Barhate Abhay Tale Amit Gudadhe Department of Artificial Intelligence and Machine Learning Faculty of Engineering & Technology Datta Meghe Institute of Higher Education & Research Wardha Maharashtra India Department of Artificial Intelligence and Data Science Faculty of Engineering & Technology Datta Meghe Institute of Higher Education & Research Wardha Maharashtra India Department of Basic Sciences and Humanities Faculty of Engineering & Technology Datta Meghe Institute of Higher Education & Research Sawangi (Meghe) Wardha Maharashtra India

ISBN: (数字)9798331528713

ISBN: (纸本)9798331528720

Soil moisture is an important parameter in the hydrological cycle and an important component for agricultural production and prediction. The regular monitoring of soil moisture plays a big role in the prediction and management of crops and droughts in a region. Climate change has led to irregular patterns in rainfall and extreme weather conditions worldwide. This has led to the farmers taking extreme steps. The traditional method is the best method of measuring soil moisture, but it requires a lot of time and is labor-intensive. Therefore, satellite remote sensing provides an alternative approach to measuring soil moisture covering a large area within less time. The application of satellite remote sensing provides comparative results to traditional methods. This study provides a discussion on spectral indices obtained from satellite data for measuring soil moisture. The current study also provides insights about the applications, challenges, and advantages of using satellite remote sensing for monitoring of soil moisture using remote sensing datasets. The study may be helpful to farmers and government agencies for planning and management of crops and water preservation over the drought prone regions of India.

关键词： Soil measurements Moisture measurement Soil moisture Droughts Crops Satellite images Monitoring Remote sensing Farming Vegetation mapping Indexes Climate change Normalized difference vegetation index

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Evaluating the Impact of Special Characters on Password Security: a Comparative Analysis

引用

IEEE Network 2025年

作者： He, Daojing Liu, Zhiyong Chan, Sammy Zhou, Beibei Guizani, Mohsen Harbin Institute of Technology School of Computer Science and Technology Shenzhen China Guizhou University State Key Laboratory of Public Big Data Guizhou China Research and Development Center Guangzhou Jingyuan Security Technology Co. Ltd Guangzhou China City University of Hong Kong Kowloon Department of Electrical Engineering Hong Kong Hong Kong Shanghai Urban Construction Vocational College School of Artificial Intelligence Application Shanghai China Machine Learning Department Abu Dhabi United Arab Emirates

Text-based password authentication remains essential for identity verification on internet platforms and blockchain systems. However, as computational power advances, security threats to user passwords have intensified, raising concerns about their effectiveness. While special characters (i.e., non-alphanumeric symbols, which include punctuation marks, mathematical symbols, and other non-letter/number characters like '.', '@', '#', '$', and similar) are often recommended to enhance password strength, the National Institute of Standards and technology (NIST) prioritizes length over complexity, challenging their necessity. This study fills a critical gap in understanding user behavior regarding special character usage. By analyzing 15 datasets from Chinese, English, and German languages, we examined patterns of special character incorporation and their impact on password strength. Our findings indicate that users frequently employ predictable methods for including special characters, potentially compromising security, especially under stringent policies. We present three key contributions: (1) an analysis of special character usage patterns, (2) a quantitative assessment of strength differences using the PCFGv4.1 model, and (3) actionable recommendations for stakeholders to enhance password security practices. This research further advocates for aligning password policies with user behavior for more effective security. © 1986-2012 IEEE.

关键词： Authentication Protocol

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Prototypical Self-Explainable Models Without Re-training

arXiv

引用

arXiv 2023年

作者： Gautam, Srishti Boubekki, Ahcene Höhne, Marina M.C. Kampffmeyer, Michael C. Department of Physics and Technology UiT The Arctic University of Norway Norway Machine Learning and Uncertainty Physikalisch-Technische Bundesanstalt Germany Data Science in Bioeconomy Leibniz Institute for Agriculture and Bioeconomy Institute for Computer Science University of Potsdam Germany

Explainable AI (XAI) has unfolded in two distinct research directions with, on the one hand, post-hoc methods that explain the predictions of a pre-trained black-box model and, on the other hand, self-explainable models (SEMs) which are trained directly to provide explanations alongside their predictions. While the latter is preferred in safety-critical scenarios, post-hoc approaches have received the majority of attention until now, owing to their simplicity and ability to explain base models without retraining. Current SEMs, instead, require complex architectures and heavily regularized loss functions, thus necessitating specific and costly training. To address this shortcoming and facilitate wider use of SEMs, we propose a simple yet efficient universal method called KMEx (K-Means Explainer), which can convert any existing pre-trained model into a prototypical SEM. The motivation behind KMEx is to enhance transparency in deep learning-based decision-making via class-prototype-based explanations that are diverse and trustworthy without retraining the base model. We compare models obtained from KMEx to state-of-the-art SEMs using an extensive qualitative evaluation to highlight the strengths and weaknesses of each model, further paving the way toward a more reliable and objective evaluation of SEMs. Copyright © 2023, The Authors. All rights reserved.

关键词： Decision making

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