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Analyzing the impact of machine learning and artificial intelligence and its effect on management of lung cancer detection in covid-19 pandemic

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Materials Today: Proceedings 2022年 56卷 2213-2216页

作者： Boddu, Raja Sarath Kumar Karmakar, Partha Bhaumik, Ankan Nassa, Vinay Kumar Vandana Bhattacharya, Sumanta Department of CSE Lenora College of Engineering Andhra Pradesh Rampachodavaram India Government of West Bengal Bikash Bhawan Salt Lake W.B. Kolkata India Dept. of Applied Mathematics With Oceanology and Computer Programming Vidyasagar University Midnapore India Department of Computer Science Engineering South Point Group of Institutions Sonepat Haryana India Department of Mathematics Dasmesh Khalsa College Zirakpur India Department of Science and Technology and Biotechnology MAKAUT India

Cancer victims, particularly those with lung cancer, are more susceptible and at higher danger of COVID-19 and associated consequences as a result of their compromised immune systems, which makes them particularly sensitive. Because of a variety of circumstances, cancer patients' diagnosis, treatment, and aftercare are very complicated and time-consuming during an epidemic. In such circumstances, advances in artificial intelligence (AI) and machine learning algorithms (ML) offer the capacity to boost cancer sufferer diagnosis, therapy, and care via the use of cutting technologies. For example, using clinical and imaging data combined with machine learning methods, the researchers may be able to distinguish among lung alterations induced by corona virus and those produced by immunotherapy and radiation. During this epidemic, artificial intelligence (AI) may be utilized to guarantee that the appropriate individuals are recruited in cancer clinical trials more quickly and effectively than in the past, which was done in a conventional and complicated manner. In order to better care for cancer patients and find novel and more effective therapies, It is critical that we move beyond traditional research methods and use artificial intelligence (AI) and machine learning to update our research (ML). Artificial intelligence (AI) and machine learning (ML) are being utilised to help with several aspects of the COVID-19 epidemic, such as epidemiology, molecular research and medication development, medical diagnosis and treatment, and socioeconomics. The use of artificial intelligence (AI) and machine learning (ML) in the diagnosis and treatment of COVID-19 patients is also being investigated. The combination of artificial intelligence and machine learning in COVID-19 may help to identify positive patients more quickly. In order to understand the dynamics of an epidemic that is relevant to artificial intelligence, when used in different patient groups, AI-based algorithms can quic

关键词： COVID-19

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Teardrop Magnification: A Hybrid Linear-Fisheye Magnifier for the Border and Corner of the Screen

Teardrop Magnification: A Hybrid Linear-Fisheye Magnifier fo...

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IEEE International Symposium on Multimedia

作者： Florian Schniederjann Darius Rausch Jens Wiggenbrock Robert Mertens Dept. of Computer Science and Engineering HSW University of Applied Sciences Hamelin Germany BHH University of Applied Sciences Hamburg Germany

ISBN: (纸本)9781665471732

Eye tracking based interfaces have to solve two major problems: The midas-touch problem and accuracy. While the midas-touch problem can be tackled with innovative interaction concepts, accuracy problems result from human physiology, hardware limitations and increasing screen resolutions. The Multi Modal Interaction Concept for Efficient Input (M 2 ice) tries to tackle these problems with a hybrid linear-fisheye magnifier. This magnifier works in the center of the screen but is problematic on the borders/corners of the screen, as it only appears as a half or quarter circle, limiting the space of the enlarged area. This paper presents a novel teardrop magnification approach for border and corner cases that solves these problems by using a different shape and by shifting the user's focus, making more of the magnified area available for interaction.

关键词： Limiting Shape Gaze tracking Physiology Hardware

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EV Allocation and Charging within Parking Lots Using a Locational Marginal Pricing Mechanism

EV Allocation and Charging within Parking Lots Using a Locat...

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IEEE PES Innovative Smart Grid Technologies Conference Europe (ISGT Europe)

作者： Nataly Bañol Arias Juan S. Giraldo Juan Camilo López Gerwin Hoogsteen Johann Hurink Dept. of Electrical Engineering Mathematics and Computer Science University of Twente The Netherlands Energy Transition Studies Netherlands Organization for Applied Scientific Research (TNO) The Netherlands

Electrifying parking lots (PLs) is becoming increasingly challenging as the number of electric vehicle (EV) chargers operating within PLs increases, putting a strain on the internal electricity grid. Reinforcing the local grid can be an expensive solution. Therefore, an alternative approach is to design a mechanism that optimizes the distribution of incoming EVs along the PL infrastructure, minimizing operating costs for the PL owner and the charging costs for EV users. This paper proposes a user-aware pricing mechanism for EV charging in PLs based on locational marginal prices (LMPs), which consists of a base tariff and a location-dependent tariff. A mixed-integer second-order programming model is developed to determine the optimal charging spot for each EV upon arrival at the PL. Results show that the proposed LMP-based allocation mechanism distributes the strain more evenly throughout the local grid, increasing the EV hosting capacity of the PL, while minimizing charging costs for users. Compared to a random allocation approach, the proposed mechanism resulted in 30.9% less energy not served and 17.5% lower charging costs.

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The Field of Requirements Engineering Education

The Field of Requirements Engineering Education

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Conference on Software Engineering Education & Training (CSEE&T)

作者： Marian Daun Alicia M. Grubb Viktoria Stenkova Bastian Tenbergen Center for Robotics Technical University of Applied Sciences Würzburg-Schweinfurt Schweinfurt Germany Dept. of Computer Science Smith College Northhampton MA USA paluno University of Duisburg-Essen Essen Germany Dept. of Computer Science State University of New York Oswego NY USA

Requirements engineering (RE) is an essential part of the software development process. Good RE, among others, is the basis for high quality software, considerably reduces the risk for software projects to fail entirely or with budget-overspending and is crucial for coordinating systems and software engineering. Thus, RE education is a vital part of software engineering curricula. However, a central concept of what RE education comprise and how to best teach RE is lacking. Therefore, we conducted a systematic literature review of the field and provide a systematic map describing the state of the RE education field. Results for key trends in RE instruction of the past decade include involvement of real or realistic stakeholders, teaching predominantly elicitation as an RE activity, and increasing student factors such as motivation or communication skills.

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HYU at SemEval-2022 Task 2: Effective Idiomaticity Detection with Consideration at Different Levels of Contextualization

arXiv

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arXiv 2022年

作者： Joung, Youngju Kim, Taeuk Dept. of Industrial Security Dept. of Applied Statistics Chung Ang University Korea Republic of Dept. of Computer Science Dept. of Artificial Intelligence Hanyang University Korea Republic of

We propose a unified framework that enables us to consider various aspects of contextualization at different levels to better identify the idiomaticity of multi-word expressions. Through extensive experiments, we demonstrate that our approach based on the inter- and inner-sentence context of a target MWE is effective in improving the performance of related models. We also share our experience in detail on the task of SemEval-2022 Task 2 such that future work on the same task can be benefited from this. Copyright © 2022, The Authors. All rights reserved.

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OLBR: Optimization of Location-Based Routing Protocols in Vehicular Ad Hoc Networks

OLBR: Optimization of Location-Based Routing Protocols in Ve...

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Recent Innovation in Smart and Sustainable Technology (ICRISST), International Conference on

作者： Thangam S M Vivek Srikar Reddy Karthik Ullas N Abdul Khadar Jilani K Venkatesh J Jesy Janet Kumari A Saleem Raja Dept. of Computer Science and Engineering Amrita School of Computing Bengaluru Amrita Vishwa Vidyapeetham India IT Department College of Computing and Information Sciences University of Technology and Applied Sciences-Shinas Sultanate of Oman

ISBN: (数字)9798350309140

ISBN: (纸本)9798350309157

The proposed work employs ns-3, SUMO, and NetAnim to create geographical routing in vehicular ad hoc networks (VANETs). The research attempts to assess the performance of three well-known protocols AODV, DSDV, and OLSR in VANET settings. Utilizing SUMO’s realistic mobility models and NS3’s simulation capabilities, the project simulates node communication, protocol behaviour, and vehicle movement. The main purpose is to know how well various routing techniques work in dynamic vehicular situations.

关键词： Technological innovation Roads Vehicular ad hoc networks Packet loss Routing Routing protocols Delays Vehicle dynamics AODV Optimization

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IoT Anomaly Detection Using a Multitude of Machine Learning Algorithms

IoT Anomaly Detection Using a Multitude of Machine Learning ...

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applied Imagery Pattern Recognition Workshop (AIPR)

作者： Maria Balega Waleed Farag Soundararajan Ezekiel Xin-Wen Wu Alicia Deak Zaryn Good Dept. of Math and Computer Science Indiana University of Pennsylvania Indiana PA U.S.A Dept. of Computer Science University of Mary Washington Fredericksburg VA U.S.A

The security of IoT (Internet of Things) systems is crucial yet challenging. Anomaly detection can help assess and improve the security of these devices and systems. The detection of anomalous traffic can be performed with the use of machine learning algorithms. Gradient Boosting is a Machine Learning (ML) technique that handles both regression and classification problems and uses decision tree algorithms to produce a prediction model. eXtreme Gradient Boosting (XGBoost) is a unique implementation of Gradient Boosting that has shown very good performance and outcomes in various problems. In this paper, XGBoost’s classification abilities are examined when applied to the adopted IoT-23 dataset to see how well anomalies can be identified and what type of anomaly exists in IoT systems. Moreover, the results obtained from XGBoost are compared to other ML methods including Support Vector Machines (SVM) and Deep Convolutional Neural Networks (DCNN). The classification results were assessed based on accuracy, precision, recall, and various other performance metrics. Our experimental results showed that XGBoost outperformed both SVM and DCNN achieving accuracies up to 99.98%. In addition, XGBoost proved to be the most efficient method with respect to execution time.

关键词： Support vector machines Performance evaluation Machine learning algorithms Predictive models Boosting Prediction algorithms Security

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Mitigating Crop Losses: AI-enabled Disease Detection in Tomato Plants 20

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

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20th International Conference on Frontiers of Information Technology, FIT 2023

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

ISBN: (纸本)9798350395785

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. © 2023 IEEE.

关键词： Crops

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Performance Evaluation of Popular Deep Neural Networks for Neural Machine Translation 20

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

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20th International Conference on Frontiers of Information Technology, FIT 2023

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

ISBN: (纸本)9798350395785

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. © 2023 IEEE.

关键词： Long short-term memory

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Improving Existing VANETs by Incorporating UAVS Using Ant Colony and PSO Algorithms

Improving Existing VANETs by Incorporating UAVS Using Ant Co...

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Data Engineering and Communication Systems (ICDECS), International Conference on

作者： S Thangam Aniket Dixit Alok Kumar Karn Arnav Thakur Cg Arunbalaji J Jesy Janet Kumari A. Saleem Raja Dept. of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India IT Department College of Computing and Information Sciences University of Technology and Applied Sciences-Shinas Sultanate of Oman

Vehicular Ad Hoc Networks (VANETs) are a type of wireless communication network that enable vehicles to communicate with each other and with roadside infrastructure in a peer-to-peer fashion; despite their vast applications, they come with several shortcomings. The key challenges in their real-world implementation include scalability, connectivity, and coverage issues. Unmanned Aerial Vehicles (UAVs), also known as drones, can complement existing VANETs in several ways to enhance their functionality and address some of their limitations. This paper studies the incorporation of UAVs in VANETs to overcome the challenges faced by the present networks. The approach is based on the dynamic deployment of UAVs in the most optimal positions, found by utilizing Particle Swarm Optimization (PSO) and Ant Colony algorithms which analyze the vehicle density, and previous coverage information in the network. The deployment of UAVs is intended to provide a seamless network coverage for ground vehicles. The impact of dynamic UAV mobility in communicating VANETs is comparatively studied. The simulation is done using Network Simulator-3 (NS3) simulator to evaluate the performance of 4 VANET protocols, AODV, DSR, OLSR, and DSDV, in terms of packet delivery ratio (PDR), Average End-to-End Delay, Throughput, Average Throughput per Packet, Packet Drop Rate, and Normalized Routing Load after incorporating the proposed modification designs. The paper concludes that the incorporation of Ant Colony is better suited to enhance VANETs than PSO.

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