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Optimizing Atmospheric Water Generation Through Advanced Weather Prediction Using Solar Energy Data 9

Optimizing Atmospheric Water Generation Through Advanced Wea...

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9th IEEE International Conference for Convergence in Technology, I2CT 2024

作者： Dar, Eman Rana, Khadija Hussain, Muhammad Zunnurain Zulkifl Hasan, Muhammad Khalid, Aqsa Javaid, Arslan Altaf, Jawad Sattar, Basit Chuhan, Saad Hussain Atif Yaqub, Muhammad Qureshi, Ali Moiz Mubarak, Zaima Information Technology University Computer Engineering Department Lahore Pakistan Bahria University Lahore Campus Dept. of Computer Science Lahore Pakistan University of Central Punjab Faculty of Information Technology Lahore Pakistan National College of Business Adm. & Economics Department of Computer Science Lahore Pakistan National College of Ireland Nci Department of Computer Science Ireland Umt Department of Ai Lahore Pakistan

ISBN: (纸本)9798350394474

This research focuses on enhancing the efficiency of Atmospheric Water Generation (AWG) systems through the development of an accurate weather prediction model. Leveraging solar energy data, the project aims to forecast key weather parameters such as solar radiation, temperature, and humidity. The objective is to optimize the performance of the AWG device across diverse weather conditions. By integrating advanced weather prediction, the study endeavors to enable proactive adjustments in the AWG operation, ensuring optimal water production. This research contributes to the sustainable utilization of solar energy for atmospheric water generation, addressing water scarcity challenges with a forward-thinking and adaptive approach. © 2024 IEEE.

关键词： Solar energy

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All-in-Focus Imaging using qshiftN DTCWT and LP in the Frequency Partition Domain 9

All-in-Focus Imaging using qshiftN DTCWT and LP in the Frequ...

引用

9th International Conference on Computing for Sustainable Global Development, INDIACom 2022

作者： Rama Mohan, C. Kiran, S Vasudeva Kumar, A. Ashok Visvesvaraya Technological University Dept. of Computer Science and Engineering Belgaum India Ysr Engineering College of Yvu Dept. of Computer Science and Engineering Proddatur India Nmam Institute of Technology Dept. of Information Science and Engineering Nitte India Ysr Engineering College of Yvu Department of Physics Proddatur India

ISBN: (纸本)9789380544441

Image fusion utilizes fusion rules to combine multifocus images of two or more images of the same scene, which produces a fully focused image. The fusion of images provides vital information about the objects, which is more useful for visual perception and medical diagnosis. Directional selectivity and shift invariance characteristics are found to be very important in the image fusion process. Due to poor shift invariance and directionality, traditional wavelet-based fusion methods create ringing distortions in the fused image. In this paper, a novel multifocus image fusion algorithm based on the quarter shift dual tree complex wavelet transform (qshiftN DTCWT) and laplacian pyramid (LP) in the frequency partition (FP) domain is proposed. Firstly, the source images are decomposed into row and column coefficients with a frequency factor in the FP domain. Secondly, due to its directional and shift invariant characteristics, row and column images are collectively fused with qshiftN DTCWT, which proved to be a very efficient multiresolution transform for image fusion. Finally, to increase the effectiveness of the qshiftN DTCWT in the FP-based method, the LP algorithm is used to produce an all-in-focus image. The result of the proposed strategy reveals the good quality of the fused image due to its better directionality and also reduces artifacts with its shift invariance property. The statistical results also show a better performance of the proposed methodology than that of traditional fusion methodologies. © 2022 Bharati Vidyapeeth, New Delhi.

关键词： Image quality

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USDOT Tier-1 University Transportation Center for Advancing Cybersecurity Research and Education 1

USDOT Tier-1 University Transportation Center for Advancing ...

引用

1st IEEE International Conference on Mobility, Operations, Services and Technologies, MOST 2023

作者： Zhang, Yunpeng Jack Wang, Kailai Song, Lingguang Lendasse, Amaury Song, Houbing Herbert Han, Zhu Verma, Rakesh Silva, Arlei Rubio-Medrano, Carlos Li, Zhixia Richard Zhang, Guohui University of Houston Dept. of Information Science Technology HoustonTX United States University of Houston Dept. of Construction Management HoustonTX United States University of Maryland Baltimore County Dept. of Information Systems BaltimoreMD United States University of Houston Dept. of Electrical & Computer Engineering HoustonTX United States University of Houston Dept. of Computer Science HoustonTX United States Rice University Dept. of Computer Science HoustonTX United States Texas A&m University-Corpus Christi Dept. of Computer Science Corpus ChristiTX United States University of Cincinnati Dept. of Civil and Architectural Engineering and Construction Management CincinnatiOH United States University of Hawai'i at Mānoa Dept. Civil and Environmental Engineering HonoluluHI United States

ISBN: (纸本)9798350319958

The Transportation Cybersecurity Center for Advanced Research and Education (CYBER-CARE) is a US Department of Transportation (USDOT) Tier-1 University Transportation Center (UTC) funded in 2023. CYBER-CARE primarily focuses on the USDOT statutory research priority area of 'Reducing Transportation Cybersecurity Risks.' CYBER-CARE aims to establish a fundamental knowledge basis and explore advanced theory to mitigate the impacts of large-scale cyberattacks on transportation infrastructure and connected and automated vehicle (CAV) systems. The research projects at CYBER-CARE will develop conceptual frameworks, construct comprehensive datasets, explore novel analytical approaches, support the implementation of public policies and infrastructure investments, and build a high-quality industry workforce through education. All CYBER-CARE research projects can be organized into four thrusts: CAV cybersecurity, transportation data security, advanced traffic management system (ATMS) cybersecurity, and next-generation transportation cybersecurity systems. In addition, CYBER-CARE will accelerate industry collaborations, foster new technologies, and provide professionals with the skills and opportunities needed to become successful leaders in their fields. Notably, as CYBER-CARE will prioritize engagement with underrepresented minorities, these communities stand to benefit from professional development training in transportation cybersecurity. © 2023 IEEE.

关键词： Technology transfer

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Online Signature Recognition using Transformer Networks: An Overview 6

Online Signature Recognition using Transformer Networks: An ...

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6th IEEE International Conference on Advances in science and Technology, ICAST 2023

作者： Bharadi, Vinayak Hamdule, Zohaib Kambli, Sujan Salvi, Raj Chavan, Raj Nimbalkar, Vrishali Finolex Academy of Management and Technology Department of Information Technology Ratnagiri India Finolex Academy of Management and Technology Dept. of Computer Science and Engineering Ratnagiri India

ISBN: (纸本)9798350359817

Online signature verification remains a pivotal component in digital security, ensuring the authenticity of digital transactions and documents. This paper offers a comprehensive overview of prevailing online signature verification techniques, with a particular emphasis on methods employing Deep Learning. RNNs, with their inherent capability to process sequential data, have been instrumental in improving the accuracy and efficiency of signature verification systems. However, as the digital forgery landscape becomes increasingly sophisticated, there is a pressing need to explore advanced neural architectures. In this context, the paper introduces a transformer-based approach for signature verification. Unlike traditional models, transformers, with their self-attention mechanisms, have the potential to capture intricate signature patterns and nuances, offering a promising avenue for future research and application. © 2023 IEEE.

关键词： Recurrent neural networks

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Sentiment Analysis on Twitter Data of Omicron (B.l.l.529) using Natural Language Processing

Sentiment Analysis on Twitter Data of Omicron (B.l.l.529) us...

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2022 IEEE Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation, IATMSI 2022

作者： Datta, Suseta Mitra, Poojarini Kundu, Pritam Ministry of Electronics and Information Technology National Institute of Electronics and Information Technology Kolkata India University of Engineering & Management Dept. of Computer Applications Kolkata India University of Engineering & Management Dept. of Computer Science and Engineering Kolkata India

ISBN: (纸本)9781665477192

The COVID pandemic is causing outrageous interference in everyday life and financial activity. Close to two years after the presence of COVID, WHO allotted the variety B.l.l.529 a variety of concern, named 'Omicron'. Online diversion data assessment is created and transformed into a more renowned subject of investigation. In this paper, a sizably voluminous heap of appraisals and assessments are culminated with online redirection information. The evaluations and appearances of Twitter electronic diversion stage clients are summarised and researched by considering sentiment analysis by utilising various natural language processing techniques based on positive, negative, and neutral tweets. All potential outcomes are considered for investigating the feelings of Twitter clients. For the most part, tweets are assessed clearly, and this assessment ensures the headway of this investigation study. Different kinds of analyzers are utilised and measured. The 'TextBlob Sentiment Analyzer' has given the highest polarity score based on positivity, negativity, and neutrality rates in terms of inspiration, pessimism, and impartiality. A total dataset is fully determined and classified with all the analyzers, and a comparative result is also measured to find the ideal analyzer. It is intended to apply boosting machine learning methods to increase the accuracy of the proposed architecture before further implementation. © 2022 IEEE.

关键词： Sentiment analysis

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Investigating Online Searching Behavior Based on Google Trends in MENA Region Before and After COVID-19 53

Investigating Online Searching Behavior Based on Google Tren...

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53rd IEEE ASEE Frontiers in Education International Conference, FIE 2023

作者： Belkacem, Abdelkader Nasreddine Jamil, Nuraini Alrabaee, Saed College of It Uae University Dept. of Computer and Network Engineering Al Ain United Arab Emirates College of It Uae University Dept. of Computer Science and Software Engineering Al Ain United Arab Emirates College of It Uae University Dept. of Information Systems and Security Al Ain United Arab Emirates

ISBN: (纸本)9798350336429

The outbreak of the coronavirus disease (COVID-19) has had a profound impact on education worldwide. The rise of remote learning is one of the most significant changes in this regard, as many schools and universities were forced to close down by regional health authorities. This has also caused people to become more conservative in trade-offs between healthcare and education. Google Trends is the most common tool for analyzing online search behaviors. It is a free resource that provides information on the trends and changes in users' online interests over time based on certain terms and subjects. The online search queries on Google can be used to assess users' behaviors concerning online learning to forecast their choices regarding online education. This paper examines the frequency of users' web searches for online communication tools, courses, and learning terms. We statistically compared users in the Middle East and North Africa regions by using the volumes of searches recorded on Google Trends from January 2016 to August 2022. Moreover, we used machine learning techniques to identify differences among the keywords used. The findings statistically show that COVID-19 has led to an increase in the extent of students' attention to and interest in online learning. © 2023 IEEE.

关键词： COVID-19 Google Trends MENA Online learning Remote learning Web Behavior

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Exploratory Data Analysis using Autoviz for Machine Learning Classification Problem

Exploratory Data Analysis using Autoviz for Machine Learning...

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2024 International Conference on Emerging Innovations and Advanced Computing, INNOCOMP 2024

作者： Guru Prasad, M.S. Praveen Gujjar, J. Kotiyal, Arnav Prasanna Kumar, H.R. Devadas, Raghavendra M Jahan, Afsar Department of Computer Science and Engg. Dehradun India Faculty of Management Studies Bengaluru India Visvesvaraya Technological University PESITM Dept. of Information Science & Engg. Shivamogga India Gitam School of Technology Department of Computer Science and Engineering Gitam University Bengaluru India Department of Computer Application Dehradun India

ISBN: (纸本)9798350376470

As the field of machine learning continues to advance, the importance of effective exploratory data analysis (EDA) cannot be overstated, especially in the context of classification problems. This research paper introduces an innovative and automated approach to exploratory data analysis using Autoviz, a powerful Python library designed to streamline the visualization process for machine learning datasets. The study aims to demonstrate the efficacy of Autoviz in meaningful insights, facilitating feature understanding, and supporting data preprocessing for classification tasks. Furthermore, the paper discusses how Autoviz assists in feature selection by highlighting the relevance and importance of each feature about the target variable. This feature-centric analysis contributes to optimizing machine learning models by guiding the selection of relevant input features, thereby improving model accuracy and interpretability. Autoviz is a Python library designed to automate the exploratory data analysis (EDA) process for machine learning tasks. It is specifically tailored to generate a variety of visualizations that aid in understanding the structure and characteristics of a dataset. The results indicate that Autoviz significantly reduces the time and effort required for exploratory data analysis while maintaining the quality and dept. of insights obtained. The differences between Autoviz and Pycaret is discussed in this paper. It has been observed that AutoViz automates the process of exploring and analyzing data, while PyCaret is designed for the comparison, construction, and tuning of machine learning models. © 2024 IEEE.

关键词： Adversarial machine learning

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Exploring Computational Thinking Perspectives in Black Communities with Physiological Computing

Exploring Computational Thinking Perspectives in Black Commu...

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Black Issues in Computing Education (BICE)

作者： Myles Lewis Amanda Holloman Bryan Y. Hernández Cuevas Chris Crawford Dept. of Computer Science University of Alabama Department of Computer and Information Science and Engineering University of Florida Dept. of Computer Science University of Puerto Rico in Arecibo mlewis

ISBN: (数字)9798350372915

ISBN: (纸本)9798350372922

Physiological computing (PC) technology is becoming increasingly popular as wearable hardware improves. However, little is known regarding potential connections between hands-on experience with physiological computing technology and computation thinking perspectives. This is particularly true for black communities. Applications for physiological computing include systems such as wearable technology (e.g., Apple Watch) capable of mapping students' physiological activity to various outputs. The work discussed in this paper presents our initial steps toward filling these gaps. In particular, we share our observations of computational thinking perspectives that emerged while black high school students constructed simple programs driven by near real-time brain wave information. Our findings suggest interactive physiological data may lead to unique computational perspectives during learning activities.

关键词： computer science Education Physiology Real-time systems Hardware Filling Wearable devices

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Poster Abstract: Landing-Type Aware Multi-Drone Route Generation for Last-Mile Delivery Service

Poster Abstract: Landing-Type Aware Multi-Drone Route Genera...

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IEEE/ACM International Conference on Cyber-Physical Systems (ICCPS)

作者： JiHyun Kwon BaekGyu Kim Yi-Ying Chen Chung-Wei Lin Dept. of Electrical Engineering and Computer Science DGIST Daegu South Korea Dept. of Computer Science and Information Engineering National Taiwan University Taipei Taiwan

ISBN: (数字)9798350369274

ISBN: (纸本)9798350369281

We consider the problem of generating delivery routes for multiple drones in the last-mile delivery service. In particular, the landing type—how a parcel is to be dropped off from a drone—is explicitly modeled in terms of the landing area and the landing time, which was not considered in other drone delivery works. A Mixed Integer Linear Programming (MILP) problem is formulated to optimize the delivery route for each drone by minimizing the total delivery completion time. Our preliminary result shows that landing types affect the total delivery completion time significantly, even with a small number of drones. Therefore, it is necessary to explicitly consider the characteristics of landing types for more realistic delivery route generation of a large number of drones.

关键词： Cyber-physical systems Mixed integer linear programming Drones

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The Use of a Polarization-Agile Applicator in Hyperthermia Treatments of a Spherical Region

The Use of a Polarization-Agile Applicator in Hyperthermia T...

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2023 IEEE Conference on Antenna Measurements and Applications, CAMA 2023

作者： Dicarlofelice, Alessandro Tognolatti, Piero Bardati, Fernando University of l'Aquila Dept. of Industrial and Information Engineering and Economics L'Aquila Italy University of Rome Tor Vergata Department of Civil Engineering and Computer Science Rome Italy

ISBN: (纸本)9798350323047

A hyperthermia applicator that can independently radiate two orthogonal polarizations is proposed for a Polarization-Agile array configuration to obtain better control of power deposition. Through a numerical model of a layered sphere simulating a human head, the possibility of more efficiently limiting unwanted hot spots is demonstrated. © 2023 IEEE.

关键词： Polarization

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