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2nd International Conference on Business Analytics for Technology and Security, ICBATS 2023

作者： Tahir, Sidra Hafeez, Yaser Qamar, Farkhanda Alwakid, Ghadah Naif University of Arid Agriculture University Institute of Information Technology Rawalpindi Pakistan Applied Science Private University Applied Science Research Center Amman11937 Jordan Jouf University College of Computer and Information Sciences Department of Computer Science Sakakah42421 Saudi Arabia

ISBN: (纸本)9798350335644

Internet of Things (IoT) technology has transformed every facet of everyday life by making everything smarter. Among the large spectrum of IoT applications, IoT based smart agriculture has interested many researchers and has employed Machine Learning(ML) and IoT technology to undertake unique contributions. IoT based data-driven farm management approaches can assist enhance agricultural yields by managing input costs, decreasing losses, and using resources especially water more efficiently. The IoTs creates a large volume of data with varying properties dependent on location and time. To increase agricultural output through intelligent farm management, data must be thoroughly evaluated and processed. High-performance computing power in ML brings up new options for data-intensive science as the amount of data gathered rises;ML methods might be employed to further enhance application intelligence and usefulness. In this paper, a novel framework named Intelli-farm is proposed which is based on IoT and ML collectively and produces higher accuracy for detecting need of water in a particular farm. Experiments conducted with different training and testing ratio provided an average accuracy of 93.87%. © 2023 IEEE.

关键词： Machine learning

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RNNDRP: Developing Recurrent Neural Network Based Anticancer Drug Response Prediction Model

RNNDRP: Developing Recurrent Neural Network Based Anticancer...

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International Bhurban Conference on applied sciences & Technology, IBCAST

作者： Awais Raza Zaidi Abdul Majid Muhammad Bilal Department of Computer & Information Sciences Pakistan Institute of Engineering & Applied Sciences Islamabad Pakistan

ISBN: (数字)9798331516680

ISBN: (纸本)9798331516697

Due to the devastating nature of the cancer disease, patients have a high death rate in the worlde. For this purpose, pharmacologists, medical scientists, and biologists are making collaborating efforts to efficient medications for the treatment of cancer. In the past, all cancer types were treated with the same approach in a one-size-fits-all manner. In this research, we present Recurrent Neural Network-based Prediction Model (RNNDRP) that accurately predicts drug sensitivity on cell lines data of cancerous patients using GDSC dataset for training and testing. Our prediction model accurately predicts minimal inhibitory concentration (IC 50 ) for various drugs. Our RNNDRP model has obtained the RMSE and R 2 scores of 0.814 and 0.875, respectively. The performance has shown to be better than existing state of the art models developed for drug sensitivity. These improved results validated the model's potency due to hybrid features in addressing the problem of anticancer drug response prediction. The proposed model results might reduce the cost and time elapsed on lab tests for cancer treatment.

关键词： Drugs Training Sensitivity Recurrent neural networks Cancer treatment Predictive models Data models Gene expression Biological cells Cancer

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The Ludic Potentials of Player Data 23

The Ludic Potentials of Player Data

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10th ACM SIGCHI Annual Symposium on computer-Human Interaction in Play, CHI PLAY 2023

作者： Wallner, Günter Lankes, Michael Institute of Computer Graphics Johannes Kepler University Linz Linz Austria Department of Digital Media University of Applied Sciences Upper Austria Hagenberg Austria

ISBN: (纸本)9798400700293

Player data has become a crucial element in game development and research. However, its direct integration as a diegetic element within games remains largely unexplored. This paper aims to inspire the games community by highlighting the potential of using player data for creating new interactions and play experiences. We raise technical, design, psychological, and ethical questions and argue for using player data more boldly and ambitiously, empowering players to shape their experiences and the game through their own data. By doing so, data transforms from being an analytical to a creative asset, seamlessly enhancing the exchange between games and players. © 2023 Owner/Author.

关键词： Software design

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Representation with Minimized Max-Error in Optimal Piecewise Linear Approximation of Time Series Data 25th

Representation with Minimized Max-Error in Optimal Piecewi...

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25th International Conference on Web Information Systems Engineering, WISE 2024

作者： Zhao, Huanyu Li, Tongliang Wen, Shiting Shu, Zhenyu Deng, Ke Yang, Jian Pang, Chaoyi School of Computer Science and Technology Donghua University Shanghai China Institute of Applied Mathematics Hebei Academy of Sciences Shijiazhuang China Hebei Authentication Technology Engineering Research Center Shijiazhuang China Institute of Biology Hebei Academy of Sciences Shijiazhuang China School of Computer and Data Engineering NingboTech University Ningbo China RMIT University Melbourne Australia Macquarie University Sydney Australia

ISBN: (纸本)9789819605781

In the past two decades, Piecewise Linear Approximation under maximum error (max-error) bound (PLA∞) has been intensively studied for effective qualified representation and analysis of time series data. It divides a time series into fragments and then represents each fragment with a straight line to approximate the data points of that time slot. In this paper, to elevate the representation quality in the optimal PLA∞ results, we present a linear-time algorithm FindMin to construct the unique line representative of minimized maximum error (min-max error) for each fragment. Through extended experimental tests, we demonstrate that the proposed algorithm is very efficient in execution and achieves better performances than the state-of-the-art solutions. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Time series analysis

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Inspecting Mega Solar Plants through computer vision and Drone Technologies

Inspecting Mega Solar Plants through Computer Vision and Dro...

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2022 International Conference on Frontiers of Information Technology, FIT 2022

作者： Ahmed, Syed Umaid Affan, Muhammad Raza, Muhammad Ilyas Harris Hashmi, Muhammad National University of Computer and Emerging Sciences Department of Computer Sciences Karachi Pakistan University of Toulon SeaTech School of Engineering La Garde France Vision Innopolis University Institute of Robotics and Computer Innopolis Russia University of Alabama at Birmingham Department of Computer Science Birmingham United States

ISBN: (纸本)9798350345933

This research presents a unique approach for monitoring the large-scale grid-connected photovoltaic modules in solar power plants using state-of-art object detection YOLOv5 algorithm and classical image processing techniques. We have highlighted an integral part of the fully automated system in which a drone takes flight over the solar park and shoot the videos. Videos are preprocessed and used for trained YOLOv5 model to recognize the clean and dirty panels. The process is defined for a selected site and can be implemented using a Raspberry Pi. This system processes the images taken by drones, generates a report, and sends it to the concerned department automatically via email every day so that timely maintenance can be done for the long-life and safe operation of solar arrays. The inspection timeline for the same process was about one hundred and twenty hours, reduced to five minutes. It means that 99.93% of the time is saved through vision and robust automation techniques. © 2022 IEEE.

关键词： Drones

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Decentralized Edge-AI System for Real-Time Syrian License Plate Recognition Using Khadas Vim3 6

Decentralized Edge-AI System for Real-Time Syrian License Pl...

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6th International Youth Conference on Radio Electronics, Electrical and Power Engineering, REEPE 2024

作者： Ali, Salman Hamed, Ahmad Spasenov, Alexey Y. Berezkin, Dmitry V. Hamdoun, Omar Bauman Moscow State Technical University 'BMSTU' Computer-aided Design Systems Department Moscow Russia Bauman Moscow State Technical University 'BMSTU' Computer Systems and Networks Department Moscow Russia Higher Institute for Applied Sciences and Technology Informatics Department Damascus Syria

ISBN: (纸本)9798350382891

The growing dependence on personal vehicles has intensified challenges in traffic management, violation monitoring, and locating stolen cars. Addressing these issues, this study introduces a real-time, smart system for detecting and recognizing Syrian car license plates using surveillance camera image processing. The system is built on a decentralized architecture with edge computers, notably the Khadas Vim3 equipped with Neural Processing Units (NPUs). It utilizes two YoloV4-tiny networks trained on a dataset of over 1100 images of Syrian license plate. The first network detects cars and plates with high accuracy, while the second recognizes numbers on the plates with 87% accuracy. Adhering to Internet of Things (IoT) principles, the system processes up to 16 frames per second for detection and 8 frames per second for broadcasting results, maintaining uninterrupted operation even if an edge computer is offline, thereby providing a consistent and efficient solution for traffic management challenges. © 2024 IEEE.

关键词： License plates (automobile)

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Optimal Lot Size and Backordered Quantity Under Carbon Tax

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International Journal of applied and Computational Mathematics 2024年第4期10卷 1-16页

作者： Raiya, Geetanjali Mittal, Mandeep Department of Mathematics Amity Institute of Applied Sciences Amity University Uttar Pradesh Noida 201313 India Department of Mathematics School of Computer Science Engineering and Technology Bennett University Uttar Pradesh Greater Noida India

Researchers and industry are working together to develop low carbon inventory models that comply with carbon pricing regulations while maintaining company profits. However, this task becomes more challenging when companies also face issues related to product quality imperfections. To address this issue, a new study has developed an economic order quantity (EOQ) model that takes into account carbon emission tax, defective rates, selling prices, imperfect items, scrap items and backordering. The objective of this mathematical model is reduce overall expenses, encompassing emissions-related costs. A numerical example is come up to adorn the model, based on previous data. Sensitivity analysis has also been performed to validate the model and understand system characteristics, identifying the factors that affect total emissions. © The Author(s), under exclusive licence to Springer Nature India Private Limited 2024.

关键词： Carbon tax Defective items EOQ Optimization Shortages

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Performance analysis of OSPF and EIGRP convergence through IPsec tunnel using Multi-homing BGP connection

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Materials Today: Proceedings 2022年 62卷 4853-4861页

作者： Basit, Zeeshan Tabassum, Mujahid Sharma, Tripti Furqan, Muhammed Quadir Md, Abdul University of Technology and Applied Sciences 133 Al Khuwayral Al Janubyyah St Muscat Oman School of Computer Science and Engineering Vellore Institute of Technology Chennai India

Increasingly, networks stability and convergence time are critical parameters in overall network performance. Therefore, it is essential to compare the routing protocols performance to understand the convergence issues and future needs. Corporate networks require a better performing network with a stable connection with the remote branches. There is a need for a backup link availability for the remote branches’ connection in case of a link failure. The Internet Service Providers (ISPs) cannot guarantee a network connection without errors or link failures. To avoid network downtime, the enterprise uses a backup link, preferably with a different ISP with a failover link capability for their network. Other research conducted under the title of Routing Protocol Performance Analysis for RIP, EIGRP, OSPF compares the performance of the protocols using the parameters like delay, jitter, throughput. However, not many consider the link failure and its impact on the enterprise network. This research was conducted to analyze the problem faced by the corporate networks in case of link failure and what routing protocols they should use to minimize the network delay, jitter, convergence time and failover convergence time for better network performance when both internal and external routing protocols are used over the secure network connection using IPsec tunnel by simulating the network using GNS3. BGP multi-homing environment is implemented with multiple internet connections with different ISPs using different AS numbers. IPsec tunnel is established over the public network, and EIGRP and OSPF routing protocol performance is compared running over the IPsec tunnel. The testing parameters used in this study are delay, packet loss, initial convergence time, failover convergence time. For added security, the protocols convergence timings are recorded using MD5 authentication. The result of this study indicates that EIGRP has a better initial convergence time, and it has a better fai

关键词： Routing protocols

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Assessing the Performance of Deep Learning for Automated Gleason Grading in Prostate Cancer

Assessing the Performance of Deep Learning for Automated Gle...

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34th Medical Informatics Europe Conference, MIE 2024

作者： Müller, Dominik Meyer, Philip Rentschler, Lukas Manz, Robin Hieber, Daniel Bäcker, Jonas Cramer, Samantha Wengenmayr, Christoph Märkl, Bruno Huss, Ralf Kramer, Frank Soto-Rey, Iñaki Raffler, Johannes Faculty of Applied Computer Science University of Augsburg Germany Institute for Digital Medicine University Hospital Augsburg Germany Institute for Pathology University Hospital Augsburg Germany Institute DigiHealth Neu-Ulm University of Applied Sciences Germany Augsburg Germany BioM Biotech Cluster Development GmbH Germany

ISBN: (纸本)9781643685335

Prostate cancer is a dominant health concern calling for advanced diagnostic tools. Utilizing digital pathology and artificial intelligence, this study explores the potential of 11 deep neural network architectures for automated Gleason grading in prostate carcinoma focusing on comparing traditional and recent architectures. A standardized image classification pipeline, based on the AUCMEDI framework, facilitated robust evaluation using an in-house dataset consisting of 34,264 annotated tissue tiles. The results indicated varying sensitivity across architectures, with ConvNeXt demonstrating the strongest performance. Notably, newer architectures achieved superior performance, even though with challenges in differentiating closely related Gleason grades. The ConvNeXt model was capable of learning a balance between complexity and generalizability. Overall, this study lays the groundwork for enhanced Gleason grading systems, potentially improving diagnostic efficiency for prostate cancer. © 2024 The Authors.

关键词： Urology

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An Efficient Detection and Classification of Plant Diseases using Deep Learning Approach

An Efficient Detection and Classification of Plant Diseases ...

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2023 International Conference on Evolutionary Algorithms and Soft Computing Techniques, EASCT 2023

作者： Jagadeesan, S. Deepakraj, E. Ramalingam, Venkadeshan Venkatachalam, Ilayaraja Vivekanandan, Manojkumar Manjula, R. Vellore Institute of Technology School of Computer Science Engineering and Information Systems Vellore India University of Technology and Applied Sciences - Shinas Branch Information Technology Department Oman Vellore Institute of Technology School of Computer Science and Engineeing Vellore India School of Engineering and Applied Sciences Srm University-AP Department of Computer Science & Engineering Andhra Pradesh Amaravati India Srm Institute of Science and Technology College of Engineering and Technology Department of Computing Technology Kattankulathur India

ISBN: (纸本)9798350313413

Crop failure caused by disease-causing pests is an important problem. Farmers struggle with disease management and detection due to inadequate interventions. The goal is to create an automatic system to efficiently identify plant diseases from photos while reducing crop losses and increasing productivity. Machine learning algorithms offer a faster and cheaper alternative to visual inspection by experts. The main goal is to perform image analysis for early diagnosis and effective disease control. The use of CNN architecture for plant disease classification and detection offers a promising solution for plant health monitoring and risk mitigation. Given the threats to crop productivity and global food security, reliable methods for early detection and accurate classification are essential. CNNs enable efficient analysis of vast plant image databases, enabling accurate plant disease identification with speed and accuracy. The multi-layer CNN approach extracts feature and refines the representation, facilitating accurate prediction and accurate diagnosis. Transfer learning methods accelerate system development and allow adaptation to plant disease-specific databases. Combining computer vision algorithms with CNN architecture enables real-time monitoring, early disease detection and targeted intervention, reducing yield losses and improving crop management. This approach uses AI, image analysis, and plant pathology to solve the challenges of sustainable agriculture and plant diseases. System performance is measured by various performance metrics. © 2023 IEEE.

关键词： CNN-Based Approach Error Rate Plant disease Precision ROC

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