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Explaining Online Reinforcement learning Decisions of Self-Adaptive Systems 3

Explaining Online Reinforcement Learning Decisions of Self-A...

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3rd IEEE international conference on Autonomic Computing and Self-Organizing Systems (ACSOS)

作者： Feit, Felix Metzger, Andreas Pohl, Klaus Univ Duisburg Essen Ruhr Inst Software Technol Paluno Essen Germany

ISBN: (数字)9781665471374

ISBN: (纸本)9781665471374

Design time uncertainty poses an important challenge when developing a self-adaptive system. As an example, defining how the system should adapt when facing a new environment state, requires understanding the precise effect of an adaptation, which may not be known at design time. Online reinforcement learning, i.e., employing reinforcement learning (RL) at runtime, is an emerging approach to realizing self-adaptive systems in the presence of design time uncertainty. By using Online RL, the self-adaptive system can learn from actual operational data and leverage feedback only available at runtime. Recently, Deep RL is gaining interest. Deep RL represents learned knowledge as a neural network whereby it can generalize over unseen inputs, as well as handle continuous environment states and adaptation actions. A fundamental problem of Deep RL is that learned knowledge is not explicitly represented. For a human, it is practically impossible to relate the parametrization of the neural network to concrete RL decisions and thus Deep RL essentially appears as a black box. Yet, understanding the decisions made by Deep RL is key to (1) increasing trust, and (2) facilitating debugging. Such debugging is especially relevant for self-adaptive systems, because the reward function, which quantifies the feedback to the RL algorithm, must be defined by developers. The reward function must be explicitly defined by developers, thus introducing a potential for human error. To explain Deep RL for self-adaptive systems, we enhance and combine two existing explainable RL techniques from the machine learning literature. The combined technique, XRL-DINE, overcomes the respective limitations of the individual techniques. We present a proof-of-concept implementation of XRL-DINE, as well as qualitative and quantitative results of applying XRL-DINE to a self-adaptive system exemplar.

关键词： Knowledge engineering Uncertainty Runtime machine learning algorithms Neural networks Reinforcement learning Debugging

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Empirical Evaluation on Stock Market Forecasting via Extreme learning machine

Empirical Evaluation on Stock Market Forecasting via Extreme...

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Advance Computing and Innovative Technologies in engineering (ICACITE), international conference on

作者： Balakrishnan S Deepak Kumar Pathak Jeidy Panduro-Ramirez Dharam Buddhi B. Girimurugan Hemant Singh Pokhariya Department of Commerce Faculty of Science and Humanities SRM Institute of Science and Technology Chennai Tamil Nadu Faculty of Computer Applications Invertis University Bareilly Universidad Tecnológica del Perú Uttaranchal Institute of Technology Uttaranchal University KL Business School Koneru Lakshmaiah Education Foundation Guntur AP India Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun Uttarakhand India

Stock market forecasting is a crucial element of financial systems, yet it may be tough to anticipate marketplace trends because of the loud & non-stationary nature of the data. Deep learning is known for extracting superior conceptual properties from massive amounts of actual data without relying upon previous information, which may be useful in predicting financial activities. This paper seeks to offer a deep learning system that automatically mines the statistic laws of material and directs stock market operations using bottom neural network models and empirical mode deconstruction. This basic pattern was discovered but then dissected using the data's typical timeframe. data from stock market transactions were examined, PSO-optimized, and forecasted. Prediction efficiency can be increased by merging non stationary and exponential financial time series. If a specific trust level is met, the deep learning prediction, which is based on the study of enormous amounts of financial trade data, can determine the future trend and financial markets pricing. The empirical findings demonstrate the superior predictive efficiency of the EMD-based deep learner *** aim of this paper is to conduct an empirical evaluation regarding stock market forecasting using extreme learning machine. A detailed research methodology has been discussed in this study to conduct the research efficiently. Moreover, a thorough practical analysis and discussion was also included along with statistical interpretation to get desired research outcomes.

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A machine learning Methodology for Detecting SQL Injection Attacks

A Machine Learning Methodology for Detecting SQL Injection A...

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Technological Advancements in Computational Sciences (ICTACS), international conference on

作者： Anish Gupta Lalit Kumar Tyagi Aezeden Mohamed Zonal Head North-IT Academics iNurture Education Solutions Pvt Ltd Bengaluru Deportment of CSE GL Bajaj Institute of Technology & Management Greater Noida Mechanical Engineering Department PNG University a/Technology Papua New Guinea

In today's environment, we all rely on web applications. The number of web application users is growing by the day. Most organizations use databases to hold data about their users or information about the services they provide. Structured Query Language is frequently used for database communication. During a SQL Injection attack, the attacker runs a malicious SQL statement on the database. Since SQL injection can be used to alter database values, wipe out the entire database, and steal database content, it poses a serious security risk. Attackers can acquire unauthorized access to the entire database by using SQL injection. SQL Injection attacks are feasible when a web application fails to check or filter user- entered input. SQL injection threats are both detected and analyzed using machine learning methods such as naive Bayes, Gradient Boosting, Support Vector machine, Decision Tree, and Convolution Neural Network.

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machine learning Optimization Techniques for Accurate Melanoma Diagnosis 3

Machine Learning Optimization Techniques for Accurate Melano...

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3rd IEEE international conference on Automation and Computation, AUTOCOM 2025

作者： Sidhu, Jaspreet Marandi, Jason Katyal, Ashu Patil, Nagaraj Acharjya, Kalyan Maddikayala, Veera Nagaiah Centre of Research Impact and Outcome Chitkara University Punjab Rajpura140417 India Department of Computer Science and Engineering Odisha Bhubaneswar India Chitkara Centre for Research and Development Chitkara University Himachal Pradesh Baddi174103 India Karnataka Bangalore India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Bengaluru India

ISBN: (纸本)9798331542375

Melanoma is the deadliest form of skin cancer, claiming more than 7,000 lives each year in the United States;roughly 67, where there were almost over196 new cases URITY CASES ANNUALLY IN THE US. Because of its visual similarity to non-melanomas, it is more important than ever that melanoma be discovered early for effective treatment. As per the research, machine learning (ML) techniques have gained high popularity for automated image analysis and classification during melanoma diagnosis, and they have assisted dermatologists in diagnosing efficiently. At the same time, the performance of ML models relies on how optimization techniques are implemented in training. ML optimization tools buck as gradient descent and poor initialization may result in black-box solutions that could lead to misdiagnosis. New approaches with deep learning and reinforcement have led to dramatic improvements in the performance of machine-learning models over the last several years. Historically, deep learning algorithms have proven highly accurate in classifying melanoma images because they can automatically learn complex features from the data. In addition, they tried to optimize the feature selected and make your ML model interpretable by using the reinforcement learning method, which searches for an optimal solution through a trial-and-error process. Conclusion Optimization of machine learning techniques is essential for providing accurate melanoma diagnosis. Recent years of the digital revolution, stupefied by advanced arguments like Deep learning and Reinforcement learning models, have contributed to high accuracy in machine learning models a dream come true for dermatologists with timely detection & cement of this deadly disease. © 2025 IEEE.

关键词： Deep reinforcement learning

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A Comprehensive machine learning Approach for Wild Fire Detection and Classification Using Squeezenet and Ridge Regularized Logistic Regression

A Comprehensive Machine Learning Approach for Wild Fire Dete...

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Smart Systems for applications in Electrical Sciences (ICSSES), international conference on

作者： Kuzhaloli S L.M.I. Leo Joseph Ganesan P A. Aranganathan Gopukumar Sivakumar Thangam G. Sajiv Dept. of EEE Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai India Department of Electronics and Communication Engineering SR University Warangal Telangana India Dept. of ECE Vidya Jyothi Institute of Technology Hyderabad India Department of Electronics and Communication Engineering Sathyabama Institute of Science and Technology Chennai India Centre for Global Health Research (CGHR) Saveetha Medical College and Hospital Saveetha Institute of Medical and Technical Sciences (SIMATS) Saveetha University Chennai India Dept. of ECE Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai India

ISBN: (数字)9798331507671

ISBN: (纸本)9798331507688

Wild fire is a serious environmental and socioeconomic menace and therefore any effort aimed at the early and accurate detection of wild fire has to be very useful. This paper proposes a novel machine learning framework for wildfire detection as well as its classification utilizing SqueezeNet as a lightweight convolutional neural network (CNN) and ridgeregularized logistic regression (RR-LR) as a classifier to improve the classification performance and model stability. The proposed method is compared with a set of machine learning classifiers like kNN. Al deliberation of these experimentation results suggests that the ridge-regularized logistic regression model performs better than these methods, specifically in cases concerning overfitting and in situations where the amount or balance of data available is small. Furthermore, SqueezeNet achieves efficient feature extraction that directly lessens the computation cost so users can deploy it in real-time especially in areas with limited resources. This work reveals the benefits of combining deep feature extraction with proper regularization methods and points to a promising solution for the wildfire detection and classification problem.

关键词： Logistic regression Wildfires Accuracy Computational modeling machine learning Feature extraction Real-time systems Stability analysis Convolutional neural networks Overfitting

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machine learning Based Heatwave Modeling for Akola, Maharashtra

Machine Learning Based Heatwave Modeling for Akola, Maharash...

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Intelligent data Communication Technologies and Internet of Things (IDCIoT), international conference on

作者： Nabanita Mandal Abhinav Gajria Hussain Rangwala Tanuja Sarode Computer Engineering Department Thadomal Shahani Engineering College Mumbai India

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527556

Heatwaves are a serious challenge in India, claiming many lives yearly. The heatwaves occur when the temperature rises above the normal continuously for a few days. This paper proposes a Stacked LSTM model that predicts the maximum temperature. The study region is Akola in Maharashtra, India which is prone to heatwaves. It is observed that the Proposed Method gives less Mean Absolute Error as compared to other techniques like Multiple Linear Regression, Random Forest, and Support Vector Regression. After data collection, appropriate data pre-processing technique is applied. Model is built considering the pre-processed data which does not have any anomalies. The reason for improved result of Proposed model is its ability to handle the complex pattern present in the data and learn from it to give better predictions. Maximum temperature is selected as the target variable for prediction and the other features are humidity, dew point etc. are considered here.

关键词： Heating systems Support vector machines Predictive models Prediction algorithms Market research data models Long short term memory Tuning Random forests Overfitting

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Artificial Intelligence and Remote Work: Transforming Human Resource Management in a Post-Pandemic World

Artificial Intelligence and Remote Work: Transforming Human ...

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Automation, Computing and Renewable Systems (ICACRS), international conference on

作者： Ch Sahyaja Ch Shankar Khudsiya Zeeshan N. Nagaraj Amrita School of Business Amrita Vishwa Vidya Peetam Amaravathi India GITAM School of Business GITAM (Deemed to be) University Hyderabad India

ISBN: (数字)9798331532420

ISBN: (纸本)9798331532437

This research explores the transformative impact of Artificial Intelligence (AI) on Human Resource Management (HRM) practices within the context of remote work. By analyzing existing research and case studies, this paper investigates how AI technologies, such as machine learning and predictive analytics, can be leveraged to optimize recruitment processes, streamline performance evaluations, and facilitate seamless collaboration among geographically dispersed teams. The study also addresses the ethical considerations associated with AI in HRM, including data privacy, algorithmic bias, and ensuring fair and equitable treatment of employees. The findings of this research provide valuable insights into the potential of AI to revolutionize HRM practices in the remote work era, enabling organizations to enhance employee engagement, improve decision-making, and drive innovation.

关键词： Training Ethics data privacy Technological innovation Renewable energy sources Decision making Organizations Remote working Scheduling Artificial intelligence

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Construction and Analysis of a data-Driven T2-SMOTE-GBDT Prediction Model for Vascular Vertigo 22

Construction and Analysis of a Data-Driven T2-SMOTE-GBDT Pre...

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3rd international Symposium on Artificial Intelligence for Medical Sciences, ISAIMS 2022

作者： Song, Dengqin Yi, Tongqiang Chen, Hongci Zhang, Xiong Pu, Huiyun Peng, Shuangli Clinical School of Traditional Chinese Medicine Hubei University of Traditional Chinese Medicine China Clinical School of Power and Mechanical Engineering Wuhan Univerisity China Clinical School of Clinical School of Traditional Chinese Medicine Hubei University of Traditional Chinese Medicine China

ISBN: (纸本)9781450398442

At present, vascular vertigo has become a high incidence. Still, traditional diagnosis and treatment methods are highly dependent on the clinical experience of medical workers, and are prone to misdiagnosis, missed diagnosis, and excessive diagnosis and treatment, resulting in a waste of medical resources. Based on the popular artificial intelligence algorithm, this paper constructs a clinical prediction model of vascular vertigo based on data-driven and machine learning. In this paper, the missing values and outliers in the data samples are first processed and normalized. Then two different algorithms, Pierre coefficient and random forest, are used for feature selection. Then Smote algorithm is used to generate data to solve the data imbalance problem. Finally, we use the hyperopt algorithm to optimize the parameters of the GBDT model. Compared with other models, the prediction model proposed in this paper is better than other models. © 2022 ACM.

关键词： Diagnosis

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A Comparative Analysis of Federated learning Towards Big data IoT with Future Perspectives

A Comparative Analysis of Federated Learning Towards Big dat...

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Computing and Information Technology (ICCIT-1441), 2020 international conference on

作者： G. Anitha A. Jegatheesan E. Baburaj Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medicaland Technical Sciences Chennai India Department of Electrical and Computer Engineering College of Engineering and Technology Bule Hora University Ethiopia

Over the past few years, big data has come a long way, allowing us to make sense of the immense amounts of information being produced by cutting-edge services and apps, as well as a plethora of IoT devices. Only through extracting value from huge data through data analytics can big data's full potential be accomplished [6]. machine learning (ML) plays a highly essential part in this process because of its capacity to know patterns and then deliver insights through learning from facts. Since the data may include private information about individuals, governments, or financial accounts, using a more traditional technique raises serious concerns about data confidentiality. Recently, Federated learning (FL) seemed like a potential learning procedure that could help us get beyond this obstacle. In FL, the central ML algorithm is only given the parameters from the local devices so that it can train and make predictions on a global scale. The aforementioned problems with huge data may be amenable to resolution thanks to this aspect of FL. Furthermore, a thorough assessment of FL for big data applications has forward done, leaving a void in the literature. This article delivers a summary of FL, big data, and the motives for the procedure of FL for big data by surveying its use in big data applications. In particular, it is important to thoroughly examine the usage of FL for major big data services with applications like the smart city, remote surgery, transportation, smart grid, and social networks. Additionally, briefly discuss the most pressing problems associated with this engaging subject and the most potential approaches to fixing them.

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Personality Traits Prediction using DISC

Personality Traits Prediction using DISC

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Applied Artificial Intelligence and Computing (ICAAIC), international conference on

作者： Suvdeep Nayak Maneesha Gupta Harshit Suri Saksham Kaushik Shubham Kumar Department of Electronics and Communication Engineering Netaji Subhas University of Technology New Delhi India

ISBN: (数字)9798350375190

ISBN: (纸本)9798350375206

This study describes a novel strategy for optimizing the cultural fit assessment phase in firm hiring procedures that combines natural language processing (NLP) techniques and semi-supervised self-training learning models. Using an unlabeled dataset taken from tweets, we used preprocessing approaches and word2vec embeddings to capture semantic commonalities in textual data. K-means clustering was then used to determine the best cluster count (k=4). Working with psychology experts, we classified 10% of the dataset with the DISC psychology model to improve model training. Using semi-supervised learning, we created a hybrid model that combined Convolutional Neural Networks (CNN) and Long Short-Term Memory networks (LSTM) to efficiently use both labeled and unlabeled data. Our research seeks to streamline the cultural fit assessment in hiring procedures, allowing for more rapid and accurate candidate evaluations.

关键词： Training Visualization Sentiment analysis Accuracy Social networking (online) Psychology Semisupervised learning

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