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Modeling, Simulation & Intelligent Computing (MoSICom), International Conference on

作者： Sakshi Priyadarshni Rakshan Fathima Siddhaling Urolagin Anupkumar M. Bongale Deepak Sudhakar Dharrao Dept. of Computer Science Birla Institute of Technology and Science Pilani Dubai United Arab Emirates Dept. of Artificial Intelligence and Machine Learning Symbiosis Institute of Technology Pune India Dept. of Computer Science & Engineering Symbiosis Institute of Technology Pune India

Customer segmentation plays a crucial role in business development by providing deep insights into customer behaviors and preferences, facilitating effective and personalized marketing strategies. However, traditional segmentation methods often fall short of accurately analyzing large and complex datasets. In recent years, the application of machine learning techniques has transformed the field of customer segmentation, enabling automated analysis, enhanced accuracy, and the discovery of intricate patterns and key trends. This research paper provides a detailed review of the application of K-Means clustering in credit card companies for effective customer segmentation. It explores the benefits, challenges, and practical considerations of utilizing K-Means clustering, along with real-world case studies highlighting trends and potential advancements for using K-Means clustering in credit card customer segmentation.

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Solar Cells, Lambert W and the LogWright Functions

Solar Cells, Lambert W and the LogWright Functions

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2023 IEEE International Conference on Electrical, Computer and Energy Technologies, ICECET 2023

作者： Lankireddy, Prabhat Jeevanandam, Sibibalan Choudhary, Aditya Deshmukh, P.C. Roberts, Ken Valluri, S.R. Indian Institute of Technology Tirupati Dept. of Electrical Engineering Tirupati India Indian Institute of Technology Bombay India Center for Machine Intelligence and Data Science Mumbai India Indian Institute of Technology Tirupati Dept. of Mechanical Engineering Tirupati India Purdue University Dept. of Mechanical Engineering West LafayetteIN United States Indian Institute of Technology Tirupati Center for Atomic Molecular and Optical Sciences & Technologies Dept. of Physics Tirupati India Dayananda Sagar University Dept. of Physics Bengaluru India University of Western Ontario Dept. of Physics and Astronomy LondonON Canada King's College University of Western Ontario Dept. of Physics and Astronomy Dept. of Management Economics and Mathematics LondonON Canada

ISBN: (纸本)9798350327816

Algorithms that calculate the current-voltage (I-V) characteristics of a solar cell play an important role in processes that aim to improve the efficiency of a solar cell. I-V characteristics can be obtained from different models used to represent the solar cell, and the single diode model is a simple yet accurate model for common field implementations. However, the I-V characteristics are obtained by solving implicit equations, which involve repeated iterations and inherent errors associated with numerical methods used. Some methods use the Lambert W function to get an exact explicit formula, but often causes numerical overflow problems. The present work discusses an algorithm to calculate I-V characteristics using the LogWright function, a transformation of the Lambert W function, which addresses the problem of arithmetic overflow that occurs in the Lambert W implementation. An implementation of this algorithm is presented and compared against other algorithms in the literature. It is observed that in addition to addressing the numerical overflow problem, the algorithm based on the LogWright function offers speed benefits while retaining high precision. © 2023 IEEE.

关键词： I-V characteristics Lambert W function LogWright function Single Diode Model Solar Cell

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NPC Behavior in Games Using Unity ML-Agents: A Reinforcement learning Approach

NPC Behavior in Games Using Unity ML-Agents: A Reinforcement...

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Automation and Computation (AUTOCOM), International Conference on

作者： Revat Meshram Sachith Krishna Omkaresh Kulkarni Rutuja Rajendra Patil Gagandeep Kaur Shruti Maheshwari Dept. of Computer Science Engineering (Artificial Intelligence and Machine Learning) Vishwakarma Institute of Information Technology Pune India Symbiosis Institute of Technology Pune Symbiosis International (Deemed University) Pune India

ISBN: (数字)9798331542375

ISBN: (纸本)9798331542382

machine learning is having a significant impact on video games, from speeding up the development of new games to the development of new methods in gaming. This study discusses the impact of using ML models in the development and improvement of video games. Examples cited in this paper relate to the ways in which NPCs behave. With the aid of Unity's ML-Agents toolkit, developers can create NPCs that respond to different strategies in real time. This makes the games more exciting and complex. The paper also outlines the possibilities of single-player games, such as first-person shooters and a role-playing game, to notice NPC behavior during the gameplay. In these gaming styles, RL frameworks can be applied to NPCs to enable them to learn and adapt to the environment to create an interactive experience for the player. The conclusions put an emphasis on the evolution of video games with the advent of ML tools; as predicted, NPC behavior becomes more intuitive and less expected.

关键词： Industries Procedural generation Automation Role playing games Games Reinforcement learning Real-time systems

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SEMIPARAMETRIC EFFICIENT INFERENCE IN ADAPTIVE EXPERIMENTS

arXiv

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arXiv 2023年

作者： Cook, Thomas Mishler, Alan Ramdas, Aaditya Dept. of Mathematics and Statistics University of Massachusetts United States J.P. Morgan AI Research J.P. Morgan Chase & Co. United States Dept. of Statistics & Data Science and Machine Learning Carnegie Mellon University United States

We consider the problem of efficient inference of the Average Treatment Effect in a sequential experiment where the policy governing the assignment of subjects to treatment or control can change over time. We first provide a central limit theorem for the Adaptive Augmented Inverse-Probability Weighted estimator, which is semiparametric efficient, under weaker assumptions than those previously made in the literature. This central limit theorem enables efficient inference at fixed sample sizes. We then consider a sequential inference setting, deriving both asymptotic and nonasymptotic confidence sequences that are considerably tighter than previous methods. These anytime-valid methods enable inference under data-dependent stopping times (sample sizes). Additionally, we use propensity score truncation techniques from the recent off-policy estimation literature to reduce the finite sample variance of our estimator without affecting the asymptotic variance. Empirical results demonstrate that our methods yield narrower confidence sequences than those previously developed in the literature while maintaining time-uniform error control. © 2023, CC BY.

关键词： Inverse problems

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Facial Expression Recognition Using Multi-Block Deep CNN

Facial Expression Recognition Using Multi-Block Deep CNN

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International Conference on Circuits, Communication, Control and Computing

作者： Priyadarshini V Srinivasulu Reddy U Venkata Rami Reddy Chirra Mrudula M Suneetha M Dept. of Computer Science & Engineering National Institute of Technology Andhra Pradesh India Dept. of Computer Applications Machine Learning & Data Analytics lab National Institute of Technology Tiruchirappalli India School of Computer Science & Engineering VIT-AP University Amaravathi India Department of CSE Malla Reddy College of Engineering Telangana India Independent Researcher Amaravati India

ISBN: (数字)9798331528539

ISBN: (纸本)9798331528546

Emotions play a crucial role in decision-making, moral judgments, and other cognitive processes. The goal of this work is to identify people's facial emotions from face images. Facial expression recognition (FER), a highly diverse field with many commonalities between classes, is a challenging problem. The capabilities of current FER systems are limited in this regard. In this work, a multi-block Deep Convolutional Neural Networks (DCNN) model was proposed to identify facial expressions. Five blocks with different computational components were defined in a multi-block DCNN to extract the unique characteristics from input images. The second model (DCNN-SVM) is developed with an ensemble of SVM classifier to improve predictability and stability. The third model (DCNN-MV) is based on the majority voting with 4 classifiers SVM, KNN, Random Forest, and Decision Tree. The CK+ dataset was utilized and expanded via image data augmentation to enhance model performance and generalization. By adjusting hyperparameters, the suggested DCNN model's accuracy was investigated. The DCNN model reports an accuracy score of 97.71%. Both DCNN-SVM and DCNN-MV models achieved 97.79% accuracy. On the CK+ dataset, the proposed models demonstrated good accuracy in identifying facial expressions. When compared to existing models, the proposed model outperformed existing models in terms of accuracy.

关键词： Support vector machines Accuracy Computational modeling Face recognition Stability analysis Circuit stability Convolutional neural networks Integrated circuit modeling Thermal stability Random forests

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Dark Patterns Detection on E-Commerce Websites

Dark Patterns Detection on E-Commerce Websites

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Automation and Computation (AUTOCOM), International Conference on

作者： Aarya Sawant Amrisha Gamane Shraddha Sonawane Omkaresh Kulkarni Rutuja Rajendra Patil Gagandeep Kaur Dept. of Computer Science Engineering (Artificial Intelligence and Machine Learning) Vishwakarma Institute of Information Technology Pune India Symbiosis Institute of Technology Nagpur Campus Symbiosis International (Deemed University) Pune India

ISBN: (数字)9798331542375

ISBN: (纸本)9798331542382

Dark patterns are techniques employed on e-commerce website interfaces for the purpose of influencing users' actions to engage in undesirable behavior such as making unintentional purchases or submitting personal information when they did not want to. This is where this study seeks to address through identification of such patterns using a machine learning method. To cover false and genuine content, a dataset with 10,000 textual samples, obtained from several e-commerce sites, was compiled. Thus, for analysis, the easily interpretable classification model, Logistic Regression, was used. The results showed that the detection accuracy of the model is 92%, where precision and Recall metrics' point to the model's abilities to detect manipulative language. The results point to the ability of machine learning methods in detecting and combating fraud in cyberspace and thus promoting an ethical e-commerce market.

关键词： Measurement Logistic regression Accuracy Computational modeling Mathematical models Fraud Electronic commerce Problem-solving Protection Random forests

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Intrusion Detection in IoT Environment Using Stochastic Gradient Descent with Warm Restarts and Gated Recurrent Unit 2

Intrusion Detection in IoT Environment Using Stochastic Grad...

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2nd IEEE International Conference on data science and Network Security, ICDSNS 2024

作者： Krishnamurthy, Revatthy Alabdeli, Haider Rajani, N. Ramesh, Vankudoth Kumar, Bura Vijay Rajalakshmi Engineering College Department of Artificial Intelligence & Machine Learning Chennai India College of Technical Engineering The Islamic University Department of Computers Techniques Engineering Najaf Iraq Nitte Meenakshi Institute of Technology Dept. of Ece Bengaluru India Cvr College of Engineering Department of CSE-DS Mangalpalle India School of Computer Science and Artificial Intelligence Sr University Warangal India

ISBN: (纸本)9798350373110

The rapid growth of Internet of Things (IoT) has attracted the cybercriminals which resulted in increase of cyber-attacks on IoT devices. If the cyber-attacks are undetected for a longer time, it causes serious service interruptions, identity protection threats. To handle these issues, this research proposes a novel intrusion detection system by employing Stochastic Gradient Descent with warm Restarts and Gated Recurrent Unit (SGDR-GRU) method. The data is normalized to obtain common scaled data by using z-score normalization technique and the feature selection is performed by Stochastic Gradient Descent with warm Restarts (SGDR). The SGDR is an optimization technique that escapes local minima, reduces overfitting and improves the performance by adjusting learning rate. Finally, classification of normal and anomaly data is carried out by Gated Recurrent Unit (GRU) that deals with gradient descent problem and captures long-range dependencies. The performance of the proposed model is evaluated on NSL-KDD dataset and achieved high accuracy 99.12%, precision 99.45%, recall 99.23% in identifying intrusion over IoT devices. The proposed method surpassed existing intrusion detection models such as stacked-deep polynomial network (SDPN), Hybrid Chicken Swarm Genetic Algorithm (HCSGA). © 2024 IEEE.

关键词： Genetic algorithms

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Image Captioning-A Comprehensive Encoder-Decoder Approach on Flickr8K

Image Captioning-A Comprehensive Encoder-Decoder Approach on...

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Automation and Computation (AUTOCOM), International Conference on

作者： Mithilesh Mahajan Saanvi Bhat Madhav Agrawal Gitanjali Shinde Rutuja Rajendra Patil Gagandeep Kaur Dept. of Computer Science Engineering (Artificial Intelligence and Machine Learning) Vishwakarma Institute of Information Technology Pune India Symbiosis Institute of Technology Nagpur Campus Symbiosis International (Deemed University) Pune India

ISBN: (数字)9798331542375

ISBN: (纸本)9798331542382

This paper proposes a new encoder-decoder frame-work based on Convolutional Neural Networks (CNN) for feature extraction and Long Short-Term Memory (LSTM) networks for image caption generation. Image captioning, the process of generating descriptive textual content for images, has diverse applications, including accessibility tools for the visually impaired, virtual assistants, social media automation, and satellite imagery analysis in critical domains like defense and disaster management. The proposed model will leverage the power of CNNs for extracting high-level image features and LSTMs for generating semantically coherent captions. Rigorously evaluated on the Flickr8k dataset, the model achieved competitive BLEU scores, showing its efficacy in generating accurate and meaningful descriptions. The methodology integrates advanced preprocessing techniques and efficient training strategies, ensuring robustness and scalability. This re-search focuses on the need to use state-of-the-art machine learning techniques in the solving of real challenges, and some of the potential routes to improving image captioning systems are suggested to include multilingual capabilities and integration with larger datasets. Contributions include a clear explanation of the encoder-decoder approach, detailed metrics for evaluation, and insights into correcting limitations including dataset size and complex scene representation. This work is a step toward further progress in automated image-to-text systems, integrating precision, relevance, and adaptability.

关键词： Measurement Training Adaptation models Automation Accuracy Social networking (online) Virtual assistants Feature extraction Convolutional neural networks Long short term memory

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Towards Neuro-Symbolic AI for Assured and Trustworthy Human-Autonomy Teaming

Towards Neuro-Symbolic AI for Assured and Trustworthy Human-...

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IEEE International Conference on Trust, Privacy and Security in Intelligent Systems and Applications (TPS-ISA)

作者： Danda B. Rawat Dept. of Electrical Engineering & Computer Science (EECS) DoD Center of Excellence in AI & Machine Learning (CoE-AIML) Howard University Washington DC USA

Despite the tremendous impact and potential of Artificial Intelligence (AI) for civilian and military applications, it has reached an impasse as learning and reasoning work well for certain applications and it generally suffers from a number of challenges such as hidden biases and causality. Next, “symbolic” AI (not as efficient as “sub-symbolic” AI), offers transparency, explainability, verifiability and trustworthiness. To address these limitations, neuro-symbolic AI has been emerged as a new AI field that combines efficiency of “sub-symbolic” AI with the assurance and transparency of “symbolic” AI. Furthermore, AI (that suffers from aforementioned challenges) will remain inadequate for operating independently in contested, unpredictable and complex multi-domain battlefield (MDB) environment for the foreseeable future and the AI enabled autonomous systems will require human in the loop to complete the mission in such a contested environment. Moreover, in order to successfully integrate AI enabled autonomous systems into military operations, military operators need to have assurance that these systems will perform as expected and in a safe manner. Most importantly, Human-Autonomy Teaming (HAT) for shared learning and understanding and joint reasoning is crucial to assist operations across military domains (space, air, land, maritime, and cyber) at combat speed with high assurance and trust. In this paper, we present a rough guide to key research challenges and perspectives of neuro symbolic AI for assured and trustworthy HAT.

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Component-Based Synthesis of Structural Variants of Simulation Models for Changeable Material Flow Systems

Component-Based Synthesis of Structural Variants of Simulati...

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Simulation Winter Conference

作者： Jan Winkels Felix Özkul Robin Sutherland Jannik Löhn Sigrid Wenzel Jakob Rehof Department of Computer Science TU Dortmund University Germany Dept. Organization of Production and Factory Planning (pfp) University of Kassel Germany Lamarr Institute for Machine Learning and Artificial Intelligence Dortmund Germany

ISBN: (数字)9798331534202

ISBN: (纸本)9798331534219

Despite relevant research endeavors, modeling efforts related to the building of discrete-event simulation models for planning changeable material flow systems still limit their practical application. This is because simulation experts have to model many possible structural variants and compare them based on key performance indicators such as throughput, workload or investment costs, while also ensuring sufficient system changeability. This article presents a methodology for reducing efforts for structural variation during the experimental phase of a simulation study. Starting from a valid initial simulation model, structural variants of this simulation model are automatically generated by applying component-based software synthesis which uses combinatorial logic; thereby, a range of simulation models is provided for the user. This paper presents the outlined methodology using a case study and places it in the research context of reducing efforts associated with the design and execution of simulation experiments.

关键词： Costs Key performance indicator Buildings Throughput Software Planning Logic Investment

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