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Multi-Day Intermodal Travel Planning for Urban Cities Using Ising Machines

Multi-Day Intermodal Travel Planning for Urban Cities Using ...

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International Conference on Intelligent Transportation

作者： Siya Bao Nozomu Togawa Dept. of Computer Science and Communications Engineering Waseda University

The multi-day intermodal travel planning problem (MITPP) is an optimization problem (OP) and it generates the optimal sequences of point-of-interests (POIs) and hotels while searching for the most suitable transport modes between POIs and hotels. Conventional methods and solvers using von Neumann computers provide good approximate solutions to the OPs, but the computation time grows exponentially dealing with large-scale or complex OPs. Meanwhile, Ising machines or quantum annealing machines are non-von Neumann computers that are designed to solve complex OPs. In this paper, we focus on solving the MITPP by a two-phase Ising-based method. The first POI clustering phase aims at generating POIs clusters for sightseeing days and the second POI routing phase generates travel routes for each day with the optimal transport modes. Practical factors such as POI satisfaction, POI duration, hotel fee, and transportation fee are included in the MITPP. We map these elements onto quadratic unconstrained binary optimization (QUBO) models. For evaluation, we use a real-world dataset in Sapporo, Japan. Empirical results confirm that the proposed method can effectively solve the MITPP both in terms of solution quality and execution time and outperforms a conventional solver, a conventional method, and the latest Ising-based method.

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Reviewlogy: An Interface for Analyzing Sentiments

Reviewlogy: An Interface for Analyzing Sentiments

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Applied Intelligence and Computing (AIC), IEEE World Conference on

作者： Radhey Shyam Shilpi Khanna Kavya Srivastava Ajay Kumar Srivastava Ishika Chaudhary Dept. of Computer Science & Engineering BBD University Lucknow India Dept. of Information Technology SRMCEM Lucknow India

ISBN: (数字)9798350384598

ISBN: (纸本)9798350384604

This paper investigates the impact of early reviews on product sales by analyzing the traits of initial reviewers on major e-commerce platforms. It segments product life-cycles into three phases: early, majority, and laggards, focusing on early reviewers who post reviews during the initial stages. The paper introduces an innovative interface for sentiment analysis to identify and predict early reviewers. Quantitative analysis reveals that early reviewers often provide more insightful comments and give higher overall ratings. Their ratings and helpfulness scores significantly affect product popularity. The methodology, tested on extensive datasets from Amazon and Yelp, extracts crucial attributes of early reviewers, including purchasing patterns and social impact. The interface employs a hybrid machine learning approach combining Naive Bayes, margin-based embedding ranking models (MERM), and support vector machines (SVMs) to predict potential early reviewers accurately. This approach balances probabilistic reasoning with robust classification, ensuring comprehensive forecasting. Evaluation metrics such as precision and accuracy demonstrate the effectiveness of the models over baseline methods. Furthermore, the study enhances the understanding of how early reviews influence consumer behavior and product success in e-commerce. The interface aids in refining product marketing strategies by enabling precise targeting of early reviewers, accelerating product adoption, amplifying sales, and optimizing marketing tactics in the competitive e-commerce landscape.

关键词： Support vector machines Measurement Sentiment analysis Reviews Statistical analysis Refining Machine learning Predictive models Probabilistic logic Electronic commerce

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Using Machine Learning and Google Earth Engine to Understand Land Use and Land Cover Classifications and NO2 Levels in California

Using Machine Learning and Google Earth Engine to Understand...

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IEEE International Conference on Electro-Information Technology

作者： Benyamain Yacoob Ethan Scheys Eyiara Oladipo Andre Price Shadi Banitaan Dept. of Electrical & Computer Engineering & Computer Science University of Detroit Mercy Detroit MI United States

ISBN: (数字)9798350330649

ISBN: (纸本)9798350330656

Anthropogenic activities release pollutants into the air, which can negatively affect human health and the environment. One such pollutant is nitrogen dioxide (NO 2 ), which can contribute to smog formation, decreased crop growth and yield, and respiratory damage. This study aimed to find a relationship between land use/land cover (LULC) classifications and NO 2 levels in the air. We used Google Earth Engine (GEE) to collect LULC and air quality data using the Google Dynamic World and the Sentinel-5P NRTI NO 2 datasets. We focused on Pasadena, California, as it provided a good demonstration of an urban area surrounded by greenery, allowing for an adequate analysis of both forms of landscape and their impact on air quality. Random forest (RF) and decision tree (DT) classifiers were used on the provided datasets, with the estimated probability of complete coverage for each LULC type being the input features and the NO 2 density being the output label, measured in mol/m 2 . Our output labels were then discretized, classifying the categories into high and low NO 2 . The machine learning classifier found a correlative relationship between LULC and NO 2 levels, as signified by our modeled accuracy outputting a value of 85%, with an average f1 score of 86%. We performed 10-fold cross-validation to enhance the reliability of model evaluation. The results from this study suggest that machine learning models can be used to predict the changes in air quality based on changes in LULC from anthropogenic activities. With future studies confirming this relationship, inner-city green spaces may benefit mental and physical well-being.

关键词： Earth Atmospheric modeling Green products Urban areas Predictive models Air quality Internet

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Enhancing Indoor Positioning of Wireless Access Points using RSSI Fingerprints

Enhancing Indoor Positioning of Wireless Access Points using...

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IEEE International Conference on Electro-Information Technology

作者： Ahlam Al Mohammad Daniel Marku John Reedy Stephen Reedy Mina Maleki Shadi Banitaan Dept. of Electrical & Computer Engineering & Computer Science University of Detroit Mercy Detroit MI United States

ISBN: (数字)9798350330649

ISBN: (纸本)9798350330656

Location-based services in commercial and industrial settings enhance power efficiency and security by providing real-time visibility of people within designated areas. Indoor facilities, in particular, benefit from accurate indoor localization to optimize resources and ensure a safe environment. This study presents an approach to localizing wireless access points (APs) within indoor environments using a handheld smartphone. The method leverages received signal strength indicator (RSSI) scans from APs via fingerprints and employs a customized localization algorithm. Three localization algorithms—filtration, non-linear, and linear trilateration—are explored to determine the most effective means of AP estimation within commercial buildings. The filtration process includes discarding the least reliable RSSI scans, identifying the predominant floor number, and enhancing accuracy through a weighted average computation using RSSI values. Meanwhile, the trilateral linear and non-linear approaches utilize a multilateral localization algorithm to estimate the location of an AP. Results indicate that the filtration approach yields the closest estimates to the AP’s real location, with an average of 2.4 meters or 8 feet away. In contrast, non-linear trilateration is more consistently close to the AP but with a noticeably further average prediction margin, while linear trilateration exhibits skewed results due to extreme outliers. Filtration is thus the preferable method as long as sufficient data is collected for accurate estimation. In conclusion, given the available data, the filtration approach emerges as the most suitable method for AP estimation in commercial buildings, producing estimates that are heavily weighted towards the closest possible AP location. However, ensuring adequate data collection for accurate localization results is essential.

关键词： Location awareness Meters Accuracy Filtration Buildings Estimation Fingerprint recognition

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Feasibility of Wide Area Network Traffic Distribution in Cloud Computing Enabled Wireless Networks

Feasibility of Wide Area Network Traffic Distribution in Clo...

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2024 International Conference on E-Mobility, Power Control and Smart Systems, ICEMPS 2024

作者： Mimani, Sushant Pillai, Sanjaikanth E. Vadakkethil Somanathan Jindal, Mayank Jiwani, Nasmin Kiruthiga, T. GoDaddy SnohomishWA United States School of Electrical Engineering and Computer Science University of North Dakota Grand Forks United States San Jose CA United States University of The Cumberlands Dept. of Information Technology WilliamsburgKY United States Vetri Vinayaha College of Engineering and Technology Dept. of Ece Trichy India

ISBN: (纸本)9798350394399

Cloud computing enabled Wi-Fi networks provide an outstanding opportunity for corporations to distribute extensive area community (WAN) site visitors and advantage from reduced latency because of the proliferation of hyper connectivity. The feasibility of implementing a Wide Area Network (WAN) traffic distribution system in cloud computing enabled wireless networks was studied. The study aimed to determine the practicality and effectiveness of this approach in improving network performance. For the experiment, a sample network topology was designed to simulate real-world scenarios and subjected to diverse traffic loads. The results showed that the WAN traffic distribution system was indeed feasible and could effectively distribute and balance network traffic in cloud computing enabled wireless networks. Furthermore, key metrics such as network latency, packet loss, and throughput were significantly improved compared to traditional networks without the WAN distribution system. Further research and development in this area could potentially lead to improved network efficiency and enhanced user experience in the rapidly growing field of cloud computing. Those consequences reveal the feasibility of deploying WAN site visitors distribution in cloud computing-enabled wireless networks, and serves as a starting point for further study aiming to improve the performance of such networks. © 2024 IEEE.

关键词： Wide area networks

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Hybrid Vision Transformer Model for Accurate Prostate Cancer Classification in MRI Images

Hybrid Vision Transformer Model for Accurate Prostate Cancer...

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International Conference on Electrical, computer and Communication engineering (ECCE)

作者： Jesika Debnath Arafath Bin Mohiuddin Al Shahriar Uddin Khondakar Pranta Anamul Sakib Amira Hossain Md. Moniruzzaman Shanto Shamim Forhad Md. Redwan Ahmed Rezaul Haque Shafiur Rahman Dept. of Computer Science Westcliff University Irvine CA USA Department of Collage of Engineering and Technology Westcliff University CA USA Dept. of Computer Science Wright State University Dayton USA Department of Business Administration International American University CA USA Dept. of Computer Science and Engineering East West University Dhaka Bangladesh Dept. of Electrical and Electronics Engineering American International University Bangladesh Dept. of Software Engineering Daffodil International University Dhaka Bangladesh

ISBN: (数字)9798350357509

ISBN: (纸本)9798350357516

Prostate cancer is a widespread disease in men worldwide, posing challenges in early detection due to its diverse nature and the limitations of current diagnostic techniques. In this study, we have developed a new method that combines CNNs with Vision Transformers (ViTs) to improve the classification of prostate cancer using MRI images. To address data imbalance and enhance model robustness, we used dual synthetic oversampling techniques and data augmentation methods. Our preprocessing pipeline reduced noise, preserved edges, and improved local contrast without amplifying noise. For feature extraction, we utilized the GrayLevel Co-occurrence Matrix (GLCM) and shape-based features to represent complex MRI data better. Among the evaluated architectures, ConvNeXt showed the best performance. Using SMOTE, it achieved an F1-score of 97.21% and a MCC of 95.32%. With ADASYN, performance improved further, reaching an F1-score of 98.82% and an MCC of 97.86%. Additionally, we created a web-based application for realtime analysis of prostate MRI scans, which can aid in clinical decision-making. These results demonstrate the potential of our approach to improve the accuracy of prostate cancer diagnosis and provide an interpretable model for clinical use.

关键词： computer vision Accuracy Magnetic resonance imaging Noise Pipelines Transformers Feature extraction Robustness Prostate cancer Resilience

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Smart Mushroom Cultivation: Enhancing Growth Conditions through Sensor-Based Monitoring

Smart Mushroom Cultivation: Enhancing Growth Conditions thro...

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Advances in Computation, Communication and Information Technology (ICAICCIT), International Conference on

作者： Ishaan Rana Ankit Gupta Rakshit Mittal Harshita Chaudhary Shobha Tyagi Pronika Chawla Dept. of Computer Science and Engineering MRIIRS Faridabad India

Smart mushroom cultivation using remote sensing involves the integration of advanced technology to optimize the growth and productivity of mushrooms. Remote sensing techniques using arduino, sensors and cloud services are employed to monitor and manage various farming and growth related parameters in mushroom farms. Using the remote sensing of mushroom fields provides a macroscopic view of environmental conditions, helping farmers identify suitable locations for mushroom cultivation based on factors like temperature, humidity, and vegetation cover. Ground-based sensors are strategically placed within the cultivation area to measure crucial variables like temperature and humidity levels. These sensors transmit data to a central control system, allowing farmers to maintain optimal conditions for mushroom growth. Automated climate control systems can adjust parameters in response to real-time data, ensuring consistent and ideal growing conditions. This enables timely intervention, reducing crop loss and the need for chemical treatments. Furthermore, remote sensing can improve resource management. Precise water and nutrient monitoring based on sensor data can lead to more efficient resource utilization, reducing waste and operating costs. In summary, smart mushroom cultivation using remote sensing technology optimizes environmental monitoring, and resource usage. By harnessing the power of remote sensing, mushroom farmers can increase yields, reduce environmental impact, and enhance the overall sustainability and profitability of their operations.

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MetaSPICE: Metaprogramming SPICE Framework for the Design Space Exploration of PUF Circuits

MetaSPICE: Metaprogramming SPICE Framework for the Design Sp...

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International Conference on Modern Circuits and Systems Technologies (MOCAST)

作者： Athanasios Xynos Vasileios Tenentes Dept. of Computer Science & Engineering University of Ioannina Greece

Silicon Physical Unclonable Functions (PUFs) generate challenge-response pairs that are unique to each die, allowing for secure identification, authentication and cryptographic keys generation. However, designing and evaluating PUFs is a complex task that can require a vast amount of SPICE netlists simulation. In this paper, we present MetaSPICE, which is a novel metaprogramming SPICE framework for the design space exploration of PUFs. Metaprogramming refers to the process of writing programs that manipulate other programs, data structures or even themselves. The designer in MetaSPICE, instead of defining multiple SPICE netlists, has to define a single SPICE template that is used to generate multiple netlists. The MetaSPICE is complementary to classical SPICE simulators, and enables the design exploration impact on PUFs’ performance. The framework consists of a template engine for generating SPICE models from SPICE templates, and software for evaluating the performance of PUF circuits in terms of uniqueness, uniformity and reliability from SPICE simulation results. We demonstrate the effectiveness of MetaSPICE by evaluating the performance and presenting improvements on a recently proposed PUF design.

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Enhancing Multi-label text classification using adaptive promptify concepts

Enhancing Multi-label text classification using adaptive pro...

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International Conference on Computing and Networking Technology (ICCNT)

作者： Veerababu Reddy Usha Rani Uppukonda N. Veeranjaneyulu Dept. of Computer Science & Engineering Vignan’s Foundation for Science Technology & Research Guntur India Dept. of Computer Science & Engineering Vignan’s Lara Institute of Technology & Science College Guntur India Dept. of Information Technology Vignan’s Foundation for Science Technology & Research Guntur India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

In present days, Multi Label Text Classification (MLTC) has become an important research area in Natural Language Processing (NLP). Existing models of MLTC have been facing domain specificity and fine-tuning challenges. Hence a model is developed to overcome the above said challenges by introducing Prompt engineering, GPT FineTuning methods and Pipeline algorithms. The experimental results are obtained based on the present Generative pre trained data produces labels for various text documents and files with an exceptional accuracy of 85%. The outcomes of the experiments reveal notable enhancements over the baseline models.

关键词： Surveys Accuracy Social networking (online) Text categorization Pipelines Medical services Natural language processing Prompt engineering Optimization Sorting

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IRS Controlling Method With Machine Learning Based on Position Information

IRS Controlling Method With Machine Learning Based on Positi...

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IEEE Global Conference on Consumer Electronics (GCCE)

作者： Yoshihiko Tsuchiya Norisato Suga Kazunori Uruma Masaya Fujisawa Dept. of Information and Computer Technology Tokyo University of Science Tokyo Japan Dept. of Information and Communication Engineering Shibaura Institute of Technology Tokyo Japan Dept. of Computer Science Kogakuin University Tokyo Japan

ISBN: (数字)9798350355079

ISBN: (纸本)9798350355086

Intelligent Reflecting Surfaces (IRS) is attracting attention for wireless communications at high-frequency band. IRS can control radio propagation by reflecting radio waves and shifting their phase. As one of the methods to control IRS, location-based methods have been proposed. However, to achieve highly accurate IRS control by the methods, the assumed propagation model must match the actual propagation environment. This study proposes a method to control the IRS from location information adapted to the propagation environment by using machine learning. Numerical experiments confirm that the proposed method can appropriately control IRS regardless of the propagation environment.

关键词： Wireless communication Adaptation models Accuracy Surface waves Machine learning Radio propagation Numerical models Consumer electronics

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