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Electronic Market for Farmers 14

Electronic Market for Farmers

引用

14th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2023

作者： Vidhya, S.G. Dhruva, M.S. Gowda, Nisha J. Shilpa Shree, H. Gowda, Shravana P. Dept. of Information Science and Engineering BGSIT India Dept. of Computer Science and Engineering BGSIT India

The Covid-19 pandemic is a global health crisis that has affected everyone. One negative impact has left local farmers struggling to find other means of income besides marketing their produce to customers. It is difficult for customers to find good quality fresh goods in super markets. The objective of this paper is two-part: first, to evaluate the existing and manual system of how consumers buy their fresh produce during the Covid19 pandemic, to design the and implement the delivery system for local farmers to sell fresh product through mobile app. © Grenze Scientific Society, 2023.

关键词： Sales

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A Systematic Literature Review of the Application of Agile Applied to Large-Scale, Safety-Critical, Cyber-Physical systems

A Systematic Literature Review of the Application of Agile A...

引用

2024 IEEE International Conference on Data and Software engineering, ICoDSE 2024

作者： Yeman, Robin J. Malaiya, Yashwant K. Dept. Systems Engineering Colorado State University Fort Collins United States Dept. Computer Science Colorado State University Fort Collins United States

ISBN: (纸本)9798331506407

The application of Agile methodologies to large-scale, safety-critical cyber-physical systems (LS/SC/CPS) has shown significant interest over the last 5 years. Although there has been limited research into each of the individual attributes for development projects that are large-scale, safety-critical, or cyber-physical systems, there is a scarcity of research when all three attributes exist simultaneously in a development endeavor. This systematic literature review explores the extent of Agile adoption in projects where all three attributes are present. It begins by examining the extent of Agile adoption by domain, identifying the drivers for change, reviewing the challenges encountered, and investigating the augmentations used to address these challenges. The findings indicate that there has been growth in adoption across the medical, aerospace, automotive, and robotics industries, with limited adoption in energy and transportation. Key drivers include speed, adaptability, and cost. Several challenges have been identified, with regulatory/compliance requirements being the most significant. Multiple augmentations to Agile have been applied to overcome the identified challenges, ranging from incorporating safety frameworks and specialized artifacts to leveraging modeling and simulation, as well as reimagining programmatics. This review highlights industry efforts to apply Agile to LS/SC/CPS, while identifying several gaps in the literature. The largest gap is empirical validation in real-world settings. Future research should focus on improving the existing Agile scaling frameworks with guidance and resources to help achieve the benefits of Agile in LS/SC/CPS development while ensuring regulatory and safety compliance. © 2024 IEEE.

关键词： Resource valuation

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GENAIR: GENERATIVE AI FOR RESILIENT URBAN AIR MOBILITY WITH VTOLS IN DISASTER EVACUATION

GENAIR: GENERATIVE AI FOR RESILIENT URBAN AIR MOBILITY WITH ...

引用

2024 Winter Simulation Conference, WSC 2024

作者： Alsaheal, Abdullah Hoy, William Haro, Priscila Rehman, Asad Celik, Nurcin Dept. of Industrial and Systems Engineering University of Miami Coral GablesFL United States Dept. of Mathematics and Computer Science Saint Louis University Madrid Spain

ISBN: (纸本)9798331534202

Coastal regions face growing threats, making timely and safe evacuation paramount. Current plans rely heavily on congested ground transportation, leading to delays and heightened stress levels. The emerging field of Urban Air Mobility (UAM), utilizing Vertical Take-Off and Landing Vehicles (VTOLs), promises to alleviate these issues by providing solutions for quick evacuation strategies. Here, we aim to leverage Generative Adversarial Networks (GANs) to expand limited datasets with synthetic data specific to disaster scenarios, evacuation routes, airspace considerations, and the impact of real-time weather events, enabling robust simulation of UAM deployment in disaster evacuations. We identified two applicable scenarios: i) UAM for Extreme Weather Emergency Evacuation and ii) Hospital Evacuations using VTOLs as use cases and illustrated their impact. This research seeks to pave the way for optimized, data-driven evacuation planning with UAM and VTOLs, ultimately enhancing the safety and efficiency of evacuations in the face of extreme events. © 2024 IEEE.

关键词： Air mobility

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Project or Factorize? A Case Study of Multiview CCA and PARAFAC2 Tensor Factorization 58

Project or Factorize? A Case Study of Multiview CCA and PARA...

引用

58th Asilomar Conference on Signals, systems and computers, ACSSC 2024

作者： Chen, Jia Papalexakis, Evangelos E. University of California Dept. of Electrical and Computer Engineering Riverside United States Uuniversity of Californiav Dept. of Computer Science and Engineering Riverside United States

ISBN: (纸本)9798350354058

Consider learning the shared representations from multiple unlabeled views. Previous work either projects different views to the same space while enforcing the agreement among the projected views such as multiview canonical correlation analysis (MCCA), or factorizes different views while ensuring the common latent components across the views such as PARAFAC2, a tensor decomposition method. In this paper we first investigate a fundamental question: 'Do these two approaches learn different representations?' Preliminary numerical results suggest that in practice they do, which, in turn, begs the question of how we can leverage this observation in order to compute a superior representation. In this paper, we present a simple proof-of-concept scheme which augments MCCA with PARAFAC2 representations and vice-versa, and we demonstrate on multiple real datasets that such scheme can improve upon the baseline representation, paving the way for future research on optimally combining the strengths of projection and factorization methods for multiview representation learning. © 2024 IEEE.

关键词： Tensors

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Role of IoT in Smart Cities: A Review, Applications, Open Challenges and Solutions 3

Role of IoT in Smart Cities: A Review, Applications, Open Ch...

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3rd International Conference on Electronics and Renewable systems, ICEARS 2025

作者： Vivek, Kolla Swetha, Gurrala Kumar M, V. T. Ram Pavan Veni, Anusuri Krishna Praveen, Neti Kale, Masthan Rao Dept. of Cse Andhra Pradesh Ongole India Dept. of Cse Telangana India Kakaraparti Bhavanarayana College Dept. of Computer Science Andhra Pradesh Vijayawada India Madanapalle Institute of Technology & Science Dept. of Computer Science & Engineering - Data Science Andhra Pradesh Madanapalle517325 India Srkr Engineering College Dept. of Computer Science and Design Andhra Pradesh Bhimavaram India Dept. of Cse Andhra Pradesh Ongole India

ISBN: (纸本)9798331509675

Smart cities have emerged as a response to the fast expansion of metropolitan areas;these places strive to improve the lives of their residents while simultaneously promoting environmentally responsible growth. To maximize urban services, the Internet of Things (IoT) is an essential technology that allows for the seamless integration of sensors, devices, and systems. This allows smart cities to become a reality. With an emphasis on smart transportation, energy management, healthcare, environmental monitoring, and public safety, this paper explores the critical role of IoT in smart cities. Data security, privacy, scalability, and interoperability are some of the problems that arise when smart cities use IoT, despite the revolutionary potential it has. These obstacles are thoroughly examined in this work, along with possible solutions to these problems. This study gives a thorough overview of the IoT and its impact on smart city development, innovation, and urban resilience by focusing on the present situation, applications, and unanswered questions. © 2025 IEEE.

关键词： Digital Technology Drainage Monitoring Fire detection Smart City Waste Management

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Microwave Marvels: Design and Computational Analysis of an Ultra-thin Metamaterial Absorber for C, X and K Band 6

Microwave Marvels: Design and Computational Analysis of an U...

引用

6th International Conference on Electrical engineering and Information and Communication Technology, ICEEICT 2024

作者： Tasnim, Nowshin Rattre, Naziya Hasan Shawon, Sarowar Morshed Alam, Mohammed Shamsul Alam, Touhidul Chittagong University of Engineering & Technology Dept. of Computer Science & Engineering Chittagong4349 Bangladesh International Islamic University Chittagong Dept. of Computer Science & Engineering Chittagong4318 Bangladesh University of Science & Technology Chittagong Dept. of Computer Science & Engineering Chattogram4202 Bangladesh Universiti Kebangsaan Malaysia Bangi43600 UKM Malaysia

ISBN: (纸本)9798350385779

Metamaterial absorbers are an advancement in material science as they provide more advantages in comparison to conventional materials. Achieving miniaturization with a multilayered structure is the primary challenge here. In this study, a novel metamaterial absorber design is proposed for microwave application. The proposed design is shaped as a squad of four segments where each segment contains an octagonal split ring and a central ring. The multi-layered design of the absorber helps to secure a high absorption rate. An extensive range of research is done to determine particular measurement units, and geometric shapes with the help of CST Studio Suit 2019. Here both ground and patch are designed using copper and the separator is designed with Roger. Moreover, the thickness of the absorber is only 3.4 mm which ensures its ultra-thin and lightweight nature. With an elaborated numerical analysis, it has been shown that the proposed absorber provides 90% to 99% absorption at the C, X, and K bands. Moreover, a concise computational analysis is accomplished with FR4 lossy, Rogers RT6010LM lossy, and Rogers RT5870LM lossy as dielectric constant. The key contribution of this research is the high absorption rate, coupled with minimal thickness and simple structure. © 2024 IEEE.

关键词： Metamaterials

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Applicability of Machine Learning to Improve Mastitis Prediction in Livestock

Applicability of Machine Learning to Improve Mastitis Predic...

引用

2024 IEEE International Conference on Industrial engineering and engineering Management, IEEM 2024

作者： Shiotsu, H. Martono, N.P. Ohwada, H. Dept. of Industrial Systems and Engineering Tokyo University of Science Noda Japan

ISBN: (纸本)9798350386097

In recent years, the automation of dairy management using artificial intelligence has been sought after, with mastitis detection being one such application. Mastitis is an inflammatory response that occurs in dairy cows, leading to economic losses such as decreased milk production. Therefore, early detection is desirable. Currently, raw milk analysis devices using lactate dehydrogenase (LDH), a biomarker, are widely used for early mastitis detection. However, the use of sensor systems often results in false positives. It is common practice to refer to detection results from the past few days for the final infection judgment, which relies on the farmer’s experience, leaving room for improvement. This study aims to combine raw milk analysis devices with machine learning techniques to detect mastitis more accurately without relying on the farmer’s experience. We constructed three machine learning detection models, achieving a maximum recall of 0.89, precision of 0.81. Furthermore, the infection prediction approach proposed in this study is widely applicable and can achieve more advanced predictions when combined with related research. © 2024 IEEE.

关键词： Adversarial machine learning

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Attention Mechanism with Bayesian Decision Theory for Drug Prediction

Attention Mechanism with Bayesian Decision Theory for Drug P...

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2024 International Conference on System, Computation, Automation and Networking, ICSCAN 2024

作者： Sankaran, A. Sathiyamurthy, K. Puducherry Technological University Dept. of Computer Science and Engineering India Puducherry Technological University Faculty of Computer Science and Engineering India

ISBN: (纸本)9798331510022

Biomedical literature lacks integration of dynamic drug interaction data for personalized prescriptions. This proposed work is a new hybrid model that enhances drug safety prescriptions by taking into account individual patient features and drug interactions. It does this by hybrid attention mechanisms with Bayesian Decision Theory (BDT), which employs Bayesian statistical thinking instead of the dot-product approach. This approach is driven by the growing complexity of patient profiles and the limitations of current algorithms, which often fail to dynamically change the relevance of individual aspects. This method seeks 94% accuracy, effectively prioritizing safer drug combinations and personalizing drug recommendations. This integration leads to more consistent and customized drug prescriptions, which ultimately improve patient outcomes and advance the field of personalized healthcare. © 2024 IEEE.

关键词： Drug interactions

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Evaluating the Performance of Machine Learning Models in Handwritten Signature Verification

Evaluating the Performance of Machine Learning Models in Han...

引用

2024 Asia Pacific Conference on Innovation in Technology, APCIT 2024

作者： Akter, Tahmina Akter, Mst. Sharmin Mahmud, Tanjim Chakma, Rishita Hossain, Mohammad Shahadat Andersson, Karl Port City International University Dept. of Computer Science and Engineering Chittagong Bangladesh Rangamati Science and Technology University Dept. of Computer Science and Engineering Rangamati4500 Bangladesh University of Chittagong Dept. of Computer Science and Engineering Chittagong4331 Bangladesh Luleå University of Technology Cybersecurity Laboratory Luleå97187 Sweden

ISBN: (纸本)9798350361537

Handwritten signatures remain a widely used method for personal authentication in various official documents, including bank checks and legal papers. The verification process is often labor-intensive and time-consuming, necessitating the development of efficient methods. This study evaluates the performance of machine learning models in handwritten signature verification using the ICDAR 2011 Signature and CEDAR datasets. The investigation involves preprocessing, feature extraction using CNN architectures, and optimization techniques. The most effective models undergo a rigorous evaluation process, followed by classification using supervised ML algorithms, such as linear SVM, random forest, logistic regression, and polynomial SVM. The results indicate that the VGG16 architecture, optimized with the Adam optimizer, achieves the satisfactory performance metrics. This study demonstrates the potential of ML methodologies to enhance the efficiency and accuracy of signature verification, offering a robust solution for document authentication. © 2024 IEEE.

关键词： Decision trees

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Predictive Modeling of Cryptocurrency Prices Using Machine Learning Algorithms 15

Predictive Modeling of Cryptocurrency Prices Using Machine L...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Mahmud, Tanjim Akter, Tahmina Chakma, Rishita Barua, Tikle Hossain, Mohammad Shahadat Andersson, Karl Dept. of Computer Science and Engineering Rangamati Science and Technology University Rangamati4500 Bangladesh Dept. of Computer Science and Engineering Port City International University Chittagong Bangladesh Dept. of Computer Science and Engineering University of Chittagong Chittagong4331 Bangladesh Cybersecurity Laboratory Luleå University of Technology Luleå97187 Sweden

ISBN: (纸本)9798350370249

Cryptocurrencies, such as Bitcoin, Binance, Ethereum, FTX, and XRP, are decentralized digital assets known for their volatile nature and potential as investment instruments. Accurate price prediction is crucial for informed investment decisions. This study explores the feasibility of various modeling techniques on diverse data structures and features for predicting the prices of these cryptocurrencies. We utilize daily and high-frequency price data to classify and predict prices using deep learning and machine learning techniques, including LSTM, Bi-LSTM, GRU, linear regression, and SGD regression. Our findings indicate that daily price projections achieve an accuracy of 0.99, outperforming more complex deep learning and machine learning models. Compared to benchmark results, our approach demonstrates superior performance, with the highest scores achieved by the applied statistical methods and advanced algorithms. This research highlights the effectiveness of deep learning and machine learning models in cryptocurrency price forecasting, offering a foundation for further exploration in the field and emphasizing the significance of sample size in predictive modeling. ©2024 IEEE.

关键词： Adversarial machine learning

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