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2020 Spring Simulation Multiconference, SpringSim 2020

作者： Fisher, Andrew Adhikari, Bhisma Zhai, Chao Morgan, Joshua E. Mago, Vijay K. Giabbanelli, Philippe J. Dept. of Computer Science Lakehead University Thunder BayON Canada Dept. of Computer Science and Software Engineering Miami University OxfordOH United States

ISBN: (纸本)9781713812883

Accurately simulating viral dynamics within a human body is often computationally intensive, thus requiring dedicated computing infrastructures. This limits the use of simulations in personalized medicine since patients may not all have access to such infrastructures or the possibility of waiting weeks for predictions. Lowering the computational burden by simplifying the models is a challenge since simulation results must remain accurate in order to safely support the decision-making activities of patients. In this work, we use machine learning to predict the results of a simulation (i.e. surrogate model) and its computational resources (i.e. performance model). This allows patients to use machine learning as a computationally light proxy to simulations and to know how long an accurate simulation would take. We demonstrate that machine learning can build such surrogate and performance models with low errors for five previously developed models of HIV, while outperforming interpolation methods commonly used for surrogate modeling. © 2020 Society for Modeling & Simulation International (SCS)

关键词： Interpolation

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Avast-CTU Public CAPE Dataset

arXiv

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arXiv 2022年

作者： Bošanský, Branislav Kouba, Dominik Maňhal, Ondřej Sick, Thorsten Lisý, Viliam Křoustek, Jakub Somol, Petr Avast Software Czech Republic Artificial Intelligence Center Dept. of Computer Science Faculty of Electrical Engineering Czech Tecnical University in Prague Czech Republic

There is a limited amount of publicly available data to support research in malware analysis technology. Particularly, there are virtually no publicly available datasets generated from rich sandboxes such as Cuckoo/CAPE. The benefit of using dynamic sandboxes is the realistic simulation of file execution in the target machine and obtaining a log of such execution. The machine can be infected by malware hence there is a good chance of capturing the malicious behavior in the execution logs, thus allowing researchers to study such behavior in detail. Although the subsequent analysis of log information is extensively covered in industrial cybersecurity backends, to our knowledge there has been only limited effort invested in academia to advance such log analysis capabilities using cutting edge techniques. We make this sample dataset available to support designing new machine learning methods for malware detection, especially for automatic detection of generic malicious behavior. The dataset has been collected in cooperation between Avast software and Czech Technical University - AI Center (AIC). Copyright © 2022, The Authors. All rights reserved.

关键词： Malware

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Does color modalities affect handwriting recognition? An empirical study on Persian handwritings using convolutional neural networks

arXiv

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arXiv 2023年

作者： Zohrevand, Abbas Imani, Zahra Sadri, Javad Suen, Ching Y. Dept. of Computer Engineering Kosar University of Bojnord Bojnord Iran Camp in Labs Foundation 1 Westmount Square Bureau 1001 WestmountQCH3Z 2P9 Canada Dept. of Computer Science and Software Engineering Faculty of Engineering and Computer Science Concordia University MontrealQC Canada Center for Pattern Recognition and Machine Intelligence Concordia University MontrealH3G 1M8 Canada

Most of the methods on handwritten recognition in the literature are focused and evaluated on Black and White (BW) image databases. In this paper we try to answer a fundamental question in document recognition. Using Convolutional Neural Networks (CNNs), as eye simulator, we investigate to see whether color modalities of handwritten digits and words affect their recognition accuracy or speed? To the best of our knowledge, so far this question has not been answered due to the lack of handwritten databases that have all three color modalities of handwritings. To answer this question, we selected 13,330 isolated digits and 62,500 words from a novel Persian handwritten database, which have three different color modalities and are unique in term of size and variety. Our selected datasets are divided into training, validation, and testing sets. Afterwards, similar conventional CNN models are trained with the training samples. While the experimental results on the testing set show that CNN on the BW digit and word images has a higher performance compared to the other two color modalities, in general there are no significant differences for network accuracy in different color modalities. Also, comparisons of training times in three color modalities show that recognition of handwritten digits and words in BW images using CNN is much more efficient. © 2023, CC BY.

关键词： Color

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DeliverAI: Reinforcement Learning Based Distributed Path-Sharing Network for Food Deliveries

arXiv

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arXiv 2023年

作者： Mehra, Ashman Saha, Snehanshu Raychoudhury, Vaskar Mathur, Archana Dept. of CSIS BITS Pilani K K Birla Goa Campus Goa India Dept. of CSIS and APPCAIR BITS Pilani Goa India HappyMonk AI Bangalore India Department of Computer Science and Software Engineering Miami University Miami United States Dept. of Information Science and Engineering Nitte Meenakshi Institute of Technology India

Delivery of items from the producer to the consumer has experienced significant growth over the past decade and has been greatly fueled by the recent pandemic. Amazon Fresh, Shopify, UberEats, InstaCart, and DoorDash are rapidly growing and are sharing the same business model of consumer items or food delivery. Existing food delivery methods are sub-optimal because each delivery is individually optimized to go directly from the producer to the consumer via the shortest time path. We observe a significant scope for reducing the costs associated with completing deliveries under the current model. We model our food delivery problem as a multi-objective optimization, where consumer satisfaction and delivery costs, both, need to be optimized. Taking inspiration from the success of ride-sharing in the taxi industry, we propose DeliverAI - a reinforcement learning-based path-sharing algorithm. Unlike previous attempts for path-sharing, DeliverAI can provide real-time, time-efficient decision-making using a Reinforcement learning-enabled agent system. Our novel agent interaction scheme leverages path-sharing among deliveries to reduce the total distance traveled while keeping the delivery completion time under check. We generate and test our methodology vigorously on a simulation setup using real data from the city of Chicago. Our results show that DeliverAI can reduce the delivery fleet size by 12%, the distance traveled by 13%, and achieve 50% higher fleet utilization compared to the baselines. © 2023, CC BY.

关键词： Reinforcement learning

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A New Rhythm in A1: Convolutional Neural Networks for Music Genre Classification

A New Rhythm in A1: Convolutional Neural Networks for Music ...

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International Conference on Emerging Technologies, ICET

作者： Huzaifa Javed Usama Arshad Zain ul Abideen Raja Hashim Ali 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

Despite the significance of music genre classification in audio identification, it remains under-explored within AI research. This tool is crucial for personalized music recommendations and similar music detection. We have developed an efficient AI model that leverages Convolutional Neural Networks (CNNs), offering high-precision genre identification when integrated into a graphical user interface. The model effectively extracts audio features like Mel Frequency Cepstral Coefficients (MFCCs), zero-crossing rate, and tempo. Testing results reveal strong performance in genre prediction across diverse tracks, affirming the model's ability to discern unique characteristics of various music genres. This performance not only attests to the model's capability in discerning the unique characteristics inherent to different music genres but also suggests that it can effectively generalize to novel, unseen data. Our model lays the groundwork for future enhancements and demonstrates the potential of AI in transforming the music industry - from personalized music playlists to exploratory recommendation systems. The success of this model paves the way for more intricate applications of AI within music analysis.

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Performance comparison of a software defined and wireless sensor network

Performance comparison of a software defined and wireless se...

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2020 International Symposium on Networks, computers and Communications, ISNCC 2020

作者： Samridhi, Samridhi Liscano, Ramiro Ontario Tech University Faculty of Engineering and Applied Science Dept. of Electrical Computer and Software Engineering OshawaOntario Canada

ISBN: (纸本)9781728156286

The paper analyses the performance of a software defined wireless sensor network (SD-WSN) to a more conventional ad-hoc wireless sensor network (WSN). Recently SD-WSN has increased in popularity due to the demand of managed and service oriented sensor networks. In this analysis, we perform several simulation experiments comparing convergence time, packet reception ratio, and data delivery time for the SDN-WISE SD-WSN system and an ad-hoc WSN based the RPL routing protocol. The experimental results confirm the expected overhead required in a SD-WSN compared to an ad-hoc approach and exposes some design flaws in the SDN-WISE architecture. © 2020 IEEE.

关键词： Wireless sensor networks

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AdaptiveCloset: Reinforcement Learning in Personalized Clothing Recommendations

AdaptiveCloset: Reinforcement Learning in Personalized Cloth...

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International Conference on Emerging Technologies, ICET

作者： Omar Kashif Majeed Zain ul Abideen Usama Arshad Raja Hashim Ali Abdullah Habib Rafay Mustafa 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

The fashion industry, with its myriad choices, often overwhelms consumers. Addressing this, AdaptiveCloset introduces a groundbreaking approach to tailoring clothing suggestions by harnessing the power of reinforcement learning (RL). Unlike conventional AI methodologies in a fashion that merely suggests based on past preferences, our system dynamically ad-justs using realtime user feedback, ensuring that recommendations remain relevant and personalized. Historically, AI's fusion into the fashion realm has witnessed multiple methodologies but conspicuously lacked the RL perspective. Our research stands at this juncture, aiming to redefine online shopping experiences. Through AdaptiveCloset, we envisage a scenario where online shoppers not only receive personalized recommendations but also feel a sense of involvement, thanks to the system's feedback-oriented adaptability. This responsiveness not only augments user engagement but provides actionable intelligence for businesses, bridging the gap between consumer desires and market offerings. In this study, our focus was to ensure the robustness and adaptability of the RL environment, positioning it as a potent tool for enhancing e-commerce interactions, optimizing sales strategies, and fortifying customer loyalty.

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An Enhanced Genetic Algorithm Framework for Efficient Solutions to Capacitated Vehicle Routing Problems

An Enhanced Genetic Algorithm Framework for Efficient Soluti...

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International Conference on Emerging Technologies, ICET

作者： Usama Arshad Hamza Hasan Ellahie Zain ul Abideen Raja Hashim Ali 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

Capacitated Vehicle Routing Problems (CVRPs), a widely acknowledged NP-hard issue, pertains to the optimal routing of a limited-capacity vehicle fleet to fulfill customer demand, aiming for the least possible travel distance or cost. Despite the presence of numerous heuristic and exact approaches, the combinatorial characteristic of CVRP renders it challenging, especially for large-scale instances. This research provides an in-depth exploration of utilizing Genetic Algorithms (GAs) to address Capacitated Vehicle Routing Problems (CVRPs), a recognized and intricate optimization issue in the realm of logistics and supply chain management. Our paper concentrates on the innovative usage of GAs, a category of stochastic search methodologies inspired by natural selection and genetics, to grapple with CVRP. We put forth a fresh framework grounded in GA that infuses unique crossover and mutation operations tailor-made for CVRP. Our comprehensive computational trials on benchmark datasets suggest that our GA-centric method is proficient in deriving high-standard solutions within acceptable computational durations, surpassing multiple contemporary techniques concerning solution quality and resilience. Our results also underscore the scalability of our proposed approach, marking it as a viable choice for tackling extensive, real-world CVRPs. This paper enriches the current knowledge bank by demonstrating the prowess of GAs in deciphering complicated combinatorial optimization issues, thus offering a novel viewpoint for future advancements in crafting more robust and efficient CVRP resolutions.

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Deciphering Faces: Enhancing Emotion Detection with Machine Learning Techniques

Deciphering Faces: Enhancing Emotion Detection with Machine ...

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International Conference on Emerging Technologies, ICET

作者： Muhammad Haseeb Ishaq Rafay Mustafa Usama Arshad Zain ul Abideen Raja Hashim Ali Abdullah Habib 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

Facial emotion detection holds significant relevance across various domains, from psychology and marketing to education and security. Despite its importance, prevalent techniques often grapple with issues like low precision, susceptibility to lighting changes, obstructions, and distinct facial characteristics. Addressing these challenges, our research embarked on devising a robust and precise facial emotion detector harnessing the potential of machine learning, focusing on convolutional neural networks (CNN). Comprehensive testing revealed that our model surpasses existing state-of-the-art techniques, showcasing superior performance on benchmark datasets. The salience of our research is underscored by its profound implications for myriad real-world applications hinging on accurate facial emotion recognition. We present an enhanced model, distinguished not just by its accuracy but also its robustness, making it apt for diverse scenarios from insightful marketing initiatives and nuanced medical diagnoses to enriched educational experiences. Through this endeavor, we have accentuated the transformative capacity of machine learning in refining and redefining facial emotion detection methodologies.

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Network Intrusion Detection System Using Deep Learning Method with KDD Cup'99 Dataset

Network Intrusion Detection System Using Deep Learning Metho...

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IEEE International Symposium on Embedded Multicore Socs (MCSoC)

作者： Jesse Jeremiah Tanimu Mohamed Hamada Patience Robert Anand Mahendran Dept. Of Computer Science Bayero University Kano Kano Nigeria Software Engineering Lab. University of Aizu Aizu Japan Department of Computer Science Federal Polytechnic Bali Bali Nigeria Theoretical Computer Science Lab. Higher School of Economics Moscow Moscow Russia

ISBN: (纸本)9781665465007

This work is a deep sparse autoencoder network intrusion detection system which addresses the issue of interpretability of L2 regularization technique used in other works. The proposed model was trained using a mini-batch gradient descent technique, L1 regularization technique and ReLU activation function to arrive at a better performance. Results based on the KDDCUP'99 dataset show that our approach provides significant performance improvements over other deep sparse autoencoder Network Intrusion Detection Systems.

关键词： Deep learning Multicore processing Network intrusion detection

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