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Exploiting Partial Observability and Optimized Simple State Representations in Deep Q-Learning

Exploiting Partial Observability and Optimized Simple State ...

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Frontiers of Information Technology (FIT)

作者： Danyaal Mahmood Usama Arshad Raja Hashim Ali Zain Ul Abideen Muhammad Huzaifa Shah Talha Ali Khan Ali Zeeshan Ijaz Nisar Ali Abu Bakar Siddique Faculty of Computer Science & Engineering GIK Institute of Engg. Sciences & Technology Topi Pakistan Dept. of Technology & Software Engineering University of Europe for Applied Sciences Berlin Germany BCMaterials Basque Center for Materials Applications & Nanostructure Spain Faculty of Electronic Systems Engineering University of Regina Regina Canada

The advent of deep Q-learning has opened up new possibilities in training autonomous agents to perform intelligently in intricate settings. This research work examines the potential of deep Q-learning in the paradigmatic Snake game, which requires an agent to navigate a grid, ingest food items, and avoid collisions. We incorporated a partially observable game state, introducing a novel level of difficulty to the task. Our proposed approach utilizes a straightforward feed-forward neural network to estimate Q-values of potential actions, given the current state. The agent progressively learns to balance between exploration and exploitation, allowing it to navigate optimally in the game scenario. Most existing studies in deep Q-learning for the Snake game overlook the complexities posed by partial observability, focusing mainly on fully observable state representations. Furthermore, the majority of the investigations delve into the efficacy of complex neural network architectures, leaving a gap in the understanding of the performance of more simplistic feed-forward networks. Our study fills this gap by offering an in-depth exploration and analysis of deep Q-learning strategies, especially when applied to partially observable game states and simple neural networks. Simulations and results reveal that adopting simpler state representations can significantly boost the agent’s performance in the Snake game. A more detailed and intricate reward function is also observed to enhance the agent’s decision-making skills and overall proficiency in the game. These observations underscore the significance of optimizing state representations and reward functions for maximizing the performance of deep Q-learning agents in the Snake game.

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Investigating novel machine learning based intrusion detection models for NSL-KDD data sets

Investigating novel machine learning based intrusion detecti...

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IT and Industrial Technologies (ICIT), International Conference on

作者： Muhammad Huzaifa Shah Muhammad Abu Bakar Raja Hashim Ali Zain Ul Abideen Usama Arshad Ali Zeeshan Ijaz Nisar Ali Muhammad Imad Said Nabi Faculty of Computer Science & Engineering GIK Institute of Engg. Sciences & Technology Topi Pakistan Dept. of Technology & Software Engineering University of Europe for Applied Sciences Berlin Germany BCMaterials Basque Center for Materials Applications & Nanostructure Spain Faculty of Electronic Systems Engineering University of Regina Regina Canada

This study investigates the application of the Mutual Information (MI) feature selection technique to improve the accuracy of Machine Learning (ML) models on NSL-KDD datasets, building upon prior research. Six ML models, namely Decision Tree (DT), Logistic Regression (LR), K-Nearest Neighbor (KNN), Random Forest (RF), Naive Bayes (NB), and Support Vector Machine (SVM) with different kernels (1st, 2nd, and 3rd), are implemented for classification purposes. The proposed DT model in this study shows higher accuracy than the DT model proposed in the original paper by Ingre et al. for Intrusion Detection System (IDS). Additionally, a multi-class classification model for NSL-KDD datasets is developed, considering both normalized and non-normalized features. Interestingly, it is observed that the models trained without normalized features achieve higher accuracies compared to those trained with normalized features. Moreover, the study enhances the classification performance of the DT-based IDS using the Correlation based Feature Selection (CFS) technique for feature selection. The proposed IDS is evaluated both before and after feature selection for multi-class classification (normal and various attack types) and binary classification (normal and abnormal data).

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Leveraging AI and NLP in Chatbot Development: An Experimental Study

Leveraging AI and NLP in Chatbot Development: An Experimenta...

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Frontiers of Information Technology (FIT)

作者： Abdul Wahab Paracha Usama Arshad Raja Hashim Ali Zain Ul Abideen Muhammad Huzaifa Shah Talha Ali Khan Ali Zeeshan Ijaz Nisar Ali Abu Bakar Siddique Faculty of Computer Science & Engineering GIK Institute of Engg. Sciences & Technology Topi Pakistan BCMaterials Basque Center for Materials Applications & Nanostructure Leioa Basque Country Spain Dept. of Technology & Software Engineering University of Europe for Applied Sciences Berlin Germany Faculty of Electronic Systems Engineering University of Regina Regina Canada

In the current era of chatbots, this research delves into the advancements in AI chatbots, drawing on artificial intelligence (AI) and natural language processing (NLP) techniques to mimic human-like conversations. A particular focus is given to the potential of chatbots in facilitating multitasking dialogues, offering emotional support, and addressing complex subject matter, all the while respecting user privacy and trust. The implemented chatbot model is trained on a neural network, using Keras and TensorFlow libraries. This model’s performance indicates a considerable dependence on the dataset’s size, with larger datasets leading to better outcomes by providing more extensive language usage and context examples. Additionally, we also analyze the effect of varying architectures and hyperparameters on chatbot performance. The significance of localizing chatbots to adapt to different languages and cultures is also highlighted. While promising, the study identifies areas of improvement, suggesting future research directions in enhancing language capture techniques, expanding training datasets, and integrating emotional intelligence within chatbot systems.

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Mitigating Crop Losses: AI-enabled Disease Detection in Tomato Plants

Mitigating Crop Losses: AI-enabled Disease Detection in Toma...

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Frontiers of Information Technology (FIT)

作者： Hassan Ashfaq Usama Arshad Raja Hashim Ali Zain ul Abideen Muhammad Huzaifa Shah Talha Ali Khan Ali Zeeshan Ijaz Nisar Ali Abu Bakar Siddique Faculty of Computer Science & Engineering GIK Institute of Engg. Sciences & Technology Topi Pakistan Dept. of Technology & Software Engineering University of Europe for Applied Sciences Berlin Germany BCMaterials Basque Center for Materials Applications & Nanostructure Leioa Basque Country Spain Faculty of Electronic Systems Engineering University of Regina Regina Canada

Fostering crop health is vital for global food security, underscoring the need for effective disease detection. This research introduces an innovative artificial intelligence (AI) model designed to enhance the detection and diagnosis of diseases in tomato plants, particularly focusing on Early Blight and Late Blight. Significantly, our model leverages cutting-edge image processing techniques to improve disease detection efficiency, outperforming traditional methods in terms of speed and accuracy. Our results demonstrate an impressive model accuracy of 92.58% on training data and 86.83% on validation data, showing the effectiveness of AI in diagnosing plant diseases. These high accuracy rates underline the potential of our model for timely disease classification, allowing for immediate and appropriate interventions. However, our research also identified a potential overfitting problem in the model’s performance. To address this, we propose using regularization and data augmentation techniques to enhance the model’s generalizability on unseen data. Additionally, we delve into inherent challenges that plague AI-based plant disease detection, such as the scarcity of diverse datasets and the difficulty of achieving broad generalizability across different plant species. In identifying potential solutions for these issues, our research lays the groundwork for the wider and more practical implementation of AI technologies in agriculture.

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Performance Evaluation of Popular Deep Neural Networks for Neural Machine Translation

Performance Evaluation of Popular Deep Neural Networks for N...

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Frontiers of Information Technology (FIT)

作者： Muhammad Naeem Abu Bakar Siddique Raja Hashim Ali Usama Arshad Zain ul Abideen Talha Ali Khan Muhammad Huzaifa Shah Ali Zeeshan Ijaz Nisar Ali Faculty of Computer Science & Engineering GIK Institute of Engg. Sciences & Technology Topi Pakistan Dept. of Technology & Software Engineering University of Europe for Applied Sciences Berlin Germany BCMaterials Basque Center for Materials Applications & Nanostructure Leioa Basque Country Spain Faculty of Electronic Systems Engineering University of Regina Regina Canada

The field of Neural Machine Translation (NMT) has shown impressive performance for quick and easy communication in various languages spoken all over the world. NMT helps us by improving communication between different languages. For this purpose, different sequential models are used such as Recurrent Neural Network (RNN), Long Short Term Memory (LSTM), and Gated Recurrent Units (GRU). Analysis among these different models are important for making language translation better and choose the best model for the right job. This research investigates the performance of these models on two distinct language datasets, English-to-German and English-to-Urdu. Based on accuracy metrics, the findings reveal that GRU having test accuracy (88.22% ) outperforms RNN (87.21% ), and LSTM (85.70% )demonstrating the highest translation accuracy, followed by RNN and LSTM exhibiting comparatively lower accuracy levels.

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Studying the effects of feature selection approaches on machine learning techniques for Mushroom classification problem

Studying the effects of feature selection approaches on mach...

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IT and Industrial Technologies (ICIT), International Conference on

作者： Abu Bakar Siddique Muhammad Abu Bakar Raja Hashim Ali Usama Arshad Nisar Ali Zain Ul Abideen Talha Ali Khan Ali Zeeshan Ijaz Muhammad Imad Faculty of Computer Science & Engg GIK Institute of Engg. Sciences & Tech Khyber Pakhtunkhwa Pakistan Faculty of Computer Science & Engg AI Research Group GIK Institute of Engg. Sciences & Tech Khyber Pakhtunkhwa Pakistan Faculty of Electronic Systems Engg University of Regina Regina Canada BCMaterials Basque Center for Materials Applications & Nanostructure Leioa Basque Country Spain Dept. of Tech & Software Engg Univ. of Europe of Applied Sciences Berlin Germany

Feature selection is a critical factor affecting the performance of optimization algorithms. Without proper feature selection, optimization algorithms may suffer from slow convergence, overfitting, increased computational requirements, and longer execution times. On the other hand, omitting important features can lead to loss of relevant information, decreased accuracy, bias, and increased vulnerability to noise and outliers. This study investigates the use of genetic algorithms as a feature selection technique for a classification problem, specifically the mushrooms classification problem. Random forest is employed as the machine learning classifier, and genetic algorithms are compared with correlation as the feature selection method. The results show that genetic algorithms achieve higher accuracy, precision, recall, and F1-score compared to correlation-based feature selection. However, genetic algorithms have limitations in their applicability to specific optimization problems, the need for proper parameter setup, and longer convergence times. Despite these drawbacks, genetic algorithms prove to be superior to other feature selection techniques, particularly correlation-based approaches. This study highlights the importance of selecting appropriate feature selection techniques for optimization algorithms to improve their performance and achieve better results. In addition, this study explored the performance of various machine learning approaches on the complete mushroom dataset with 22 features and shows that genetic algorithms with feature selection as the most accurate method.

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Enhancing Content-Based Image Retrieval for Lung Cancer Diagnosis: Leveraging Texture Analysis and Ensemble Models

Enhancing Content-Based Image Retrieval for Lung Cancer Diag...

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International Conference on Image Information Processing

作者： K A Jayabalaji Bagam Laxmaiah Jayendra Gopal Thatipudi Chandraprabha K Pravin Badhe Allam Balaram Department of Software Systems Sri Krishna Arts and Science College Coimbatore Tamilnadu India Department of CSE CMR Technical Campus Kandlakoya Telangana India Department of Computer Science University of North Texas Denton Texas USA Department of Information Technology Bannari Amman Institute of Technology Sathyamangalam Tamilnadu India Department of Pharmacology Sinhgad College of Pharmacy and Swalife Biotech Pune India Department of Computer Science and Engineering MLR Institute of Technology Hyderabad Telangana India

ISBN: (数字)9798350371406

ISBN: (纸本)9798350371413

In image retrieval systems, many search engines rely on text-based methods, extracting information from descriptions and tags to retrieve relevant images. However, this conventional approach often overlooks the optical or semantic information present in photographs, leading to errors in the retrieved results. To address this issue, it is essential to consider content-based information such as intensities, edges, and pixel values when designing image retrieval systems, especially in domains like medicine. This study focuses on developing a Content-Based Image Retrieval (CBIR) system for the domain of medicine, with a particular emphasis on lung cancer detection. Early identification and treatment of lung cancer are critical for patient outcomes, making suitable computer-Aided Diagnosis (CAD) systems essential for assisting clinicians. The proposed ensemble model incorporates various feature extraction methods, including image processing techniques and wavelet transform, to harness the valuable information from images. These features are then fed into a deep learning model to assess their effectiveness in image retrieval tasks. By combining machine learning and image processing techniques, this work presents a comprehensive CBIR system capable of efficiently retrieving relevant medical images. Such advancements in image retrieval technology hold significant promise for aiding medical professionals in diagnosing and treating diseases like lung cancer promptly.

关键词： Wavelet transforms Solid modeling Design automation Computational modeling Image retrieval Lung cancer Feature extraction

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Harnessing the Power of AI With Deep Learning: Innovations and Implications

Harnessing the Power of AI With Deep Learning: Innovations a...

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Computational, Communication and Information Technology (ICCCIT), International Conference on

作者： Talluri Upender C. Sathiyamoorthy Udayakumar K D. Kirubakaran Purushothaman V Kiruba Thangam Raja Department of CSE CMR College of Engineering and Technology Medchal Hyderabad Telangana Department of Commerce Saveetha College of Liberal Arts and Sciences Saveetha Institute of Medical and Technical Sciences (SIMATS) Chennai Tamil Nadu India Department of CSE Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Tamil Nadu India Department of Computer Science and Engineering R.M.K. Engineering College Chennai Tamil Nadu India Department of Electronics and Communication Engineering Chennai Institute of Technology Chennai Tamil Nadu India Department of Software and Systems Engineering School of Computer Science Engineering and Information Systems (SCORE) VIT University Vellore Tamil Nadu India

ISBN: (数字)9798331512965

ISBN: (纸本)9798331512972

The rapid advancements in artificial intelligence (AI) and deep learning have revolutionized various sectors, enabling unprecedented levels of innovation and efficiency. This paper delves into the transformative impact of deep learning across multiple domains, highlighting key innovations and their broader implications. By examining cutting-edge applications in healthcare, finance, autonomous systems, and natural language processing, the study elucidates how deep learning models, such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs), are pushing the boundaries of what's possible. Moreover, the paper explores the integration of deep learning with emerging technologies such as the Internet of Things (IoT), edge computing, and quantum computing, underscoring the synergistic potential of these combinations. The role of deep learning in predictive analytics and personalized solutions is discussed, showcasing its ability to enhance decision-making processes and improve user experiences. Furthermore, the paper discusses the ethical considerations, challenges, and future prospects associated with the widespread adoption of deep learning technologies. The insights provided aim to foster a deeper understanding of how harnessing AI with deep learning can drive progress while addressing critical societal and technological issues.

关键词： Deep learning Technological innovation Ethics Recurrent neural networks Autonomous systems Finance Medical services Safety Internet of Things Artificial intelligence

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Skolem Meets Schanuel 47

Skolem Meets Schanuel

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47th International Symposium on Mathematical Foundations of computer science, MFCS 2022

作者： Bilu, Yuri Luca, Florian Nieuwveld, Joris Ouaknine, Joël Purser, David Worrell, James Institut de Mathématiques de Bordeaux Université de Bordeaux CNRS Talence France School of Mathematics University of the Witwatersrand Johannesburg South Africa Research Group in Algebraic Structures & Applications King Abdulaziz University Saudi Arabia Centro de Ciencias Matemáticas UNAM Morelia Mexico Max Planck Institute for Software Systems Saarland Informatics Campus Germany University of Warsaw Warsaw Poland Department of Computer Science University of Oxford United Kingdom

ISBN: (纸本)9783959772563

The celebrated Skolem-Mahler-Lech Theorem states that the set of zeros of a linear recurrence sequence is the union of a finite set and finitely many arithmetic progressions. The corresponding computational question, the Skolem Problem, asks to determine whether a given linear recurrence sequence has a zero term. Although the Skolem-Mahler-Lech Theorem is almost 90 years old, decidability of the Skolem Problem remains open. The main contribution of this paper is an algorithm to solve the Skolem Problem for simple linear recurrence sequences (those with simple characteristic roots). Whenever the algorithm terminates, it produces a stand-alone certificate that its output is correct - a set of zeros together with a collection of witnesses that no further zeros exist. We give a proof that the algorithm always terminates assuming two classical number-theoretic conjectures: the Skolem Conjecture (also known as the Exponential Local-Global Principle) and the p-adic Schanuel Conjecture. Preliminary experiments with an implementation of this algorithm within the tool Skolem point to the practical applicability of this method. © 2022 Schloss Dagstuhl- Leibniz-Zentrum fur Informatik GmbH, Dagstuhl Publishing. All rights reserved.

关键词： Automata theory

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The Orbit Problem for Parametric Linear Dynamical systems 32

The Orbit Problem for Parametric Linear Dynamical Systems

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32nd International Conference on Concurrency Theory, CONCUR 2021

作者： Baier, Christel Jantsch, Simon Lefaucheux, Engel Ouaknine, Joël Whiteland, Markus A. Funke, Florian Karimov, Toghrul Luca, Florian Purser, David Worrell, James Technische Universität Dresden Germany Max Planck Institute for Software Systems Saarland Informatics Campus Saarbrücken Germany School of Mathematics Wits University Johannesburg South Africa Research Group in Algebraic Structures and Applications King Abdulaziz University Thuwal Saudi Arabia Department of Computer Science University of Oxford United Kingdom

ISBN: (纸本)9783959772037

We study a parametric version of the Kannan-Lipton Orbit Problem for linear dynamical systems. We show decidability in the case of one parameter and Skolem-hardness with two or more parameters. More precisely, consider a d-dimensional square matrix M whose entries are algebraic functions in one or more real variables. Given initial and target vectors u, v ∈ Qd, the parametric point-to-point orbit problem asks whether there exist values of the parameters giving rise to a concrete matrix N ∈ Rd×d, and a positive integer n ∈ N, such that Nnu = v. We show decidability for the case in which M depends only upon a single parameter, and we exhibit a reduction from the well-known Skolem Problem for linear recurrence sequences, suggesting intractability in the case of two or more parameters. © 2021 Schloss Dagstuhl- Leibniz-Zentrum fur Informatik GmbH, Dagstuhl Publishing. All rights reserved.

关键词： Computability and decidability

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