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Big Mart Sales Analysis 6

Big Mart Sales Analysis

引用

6th International Conference on Computational Intelligence and Communication Technologies, CCICT 2024

作者： Kunal Singh, Muskaan Sharma, Harshit Roy, Shweta Agarwal, Kadambri Dept. of computer science and engineering ABES engineering college Ghaziabad India

ISBN: (纸本)9798350374629

Machine learning is a powerful technology that enables computer systems to recognize patterns and make prediction and choices without performing explicit programming. The basic notion behind this technology is to create models and algorithms that can take in input information, predict an outcome using statistical computation, and adjust outputs in response to fresh information. In this research paper, we examine the Big Mart Shopping Centre scenarios in order to forecast sales of various product categories and comprehend the impact of various factors on sales of various product categories. This paper focuses on leveraging machine learning techniques to predict sales for Big Mart with the aim of optimizing inventory management and maximizing revenue. Using a variety of characteristics from a dataset that Big Mart gathered, along with the methodology used to construct a prediction model, very accurate results are produced and these findings can be utilized to make decisions aimed at increasing sales and increase operational efficiency. ©2024 IEEE.

关键词： Sales

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Fairness-Aware Beam Placement Scheduling for LEO Satellites

Fairness-Aware Beam Placement Scheduling for LEO Satellites

引用

2024 IEEE Pacific Rim Conference on Communications, computers and Signal Processing, PACRIM 2024

作者： Eftetahi, Pooria Seyed Cai, Lin Sepahi, Amir University of Victoria Dept. Electrical and Computer Engineering VictoriaBC Canada

ISBN: (纸本)9798350362312

One of the primary objectives of the next-generation communication systems is to establish ubiquitous coverage. In this regard, Low Earth Orbit (LEO) satellite networks have attracted significant attention as a promising technology to achieve global coverage. However, fairly and efficiently managing resource allocation, especially beam placement scheduling, poses a significant challenge due to the large number of ground terminals and the vast coverage of LEO satellites. To address this challenge, we model the beam placement scheduling as a problem of minimizing terminal payload at the LEO satellite while considering fairness among terminals. In our proposed solution, we divide the coverage area into segments and use the advantage actor-critic (A2C) to schedule beams within each segment. Our simulations demonstrate that our approach outperforms the greedy method in terms of both throughput and fairness while improving first-time access to the LEO satellite for terminals. Moreover, it outperforms round-robin (RR) scheduling in terms of throughput. © 2024 IEEE.

关键词： Satellite communication systems

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Synergistic Use of Offshore Wind, Wave, and Photovoltaics for Maritime Hydrogen Refueling: How the Synergy Impacts the Storage Requirements? 5

Synergistic Use of Offshore Wind, Wave, and Photovoltaics fo...

引用

5th IEEE International Conference on Communications, Information, Electronic and Energy Systems, CIEES 2024

作者： Pompodakis, Evangelos E. Karapidakis, Emmanuel Hellenic Mediterranean University Dept. of Electrical and Computer Engineering Heraklion Greece

ISBN: (纸本)9798350352863

This paper investigates the combined use of offshore wind, wave energy, and solar photovoltaics for maritime hydrogen refueling stations, with a focus on their influence on storage requirements. Eastern Crete and the Galite Islands, two Mediterranean sites with significant wind, wave, and solar energy potential, were selected as case studies for the installation of such stations. The research utilizes a Storage-to-Production Ratio (SPR) index to measure the amount of storage required to ensure a stable, uninterrupted hydrogen supply throughout the year, relative to the total annual hydrogen production. The results demonstrate that integrating either wind or wave energy with solar power significantly reduces the SPR, thanks to the weak temporal correlation between solar and the other two sources. This reduction can reach up to 20%, which, for a station providing an annual hydrogen supply of 20,000 tons, translates to a 4,000-ton reduction in storage needs. Conversely, combining wind and wave energy in the Mediterranean does not achieve a similar SPR reduction due to their strong temporal correlation. © 2024 IEEE.

关键词： Hydrogen storage

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Real-Time Evaluation of Human Inertial properties in Immersive Simulators for Moth type Boats 6

Real-Time Evaluation of Human Inertial properties in Immersi...

引用

6th International Congress on Human-computer Interaction, Optimization and Robotic Applications, HORA 2024

作者： Todesca, Davide Sergenti, Carol Giulietti, Nicola Carnevale, Marco Dept. Electrical Computer and Biomedical Engineering University of Pavia Pavia Italy

ISBN: (纸本)9798350394634

The aim of this work is to present a system designed for real-time markerless evaluation of human inertial properties, to be used in a Human In The Loop (HITL) simulator of a Moth class sailboat. The method is based on human body pose estimation algorithm used for 3D skeleton reconstruction from a multi-camera acquisition. The exploitation of a markerless system enables ease of use compared to marker-based systems, and reduces signal’s disturbances compared to Inertia Measurement Units (IMUs) for body pose reconstruction. The use of multi-camera setup and the integration of YOLOv8-pose model results with image triangulation techniques reduce occlusion occurrence, measurements drift and complexity. The 3D pose obtained is then used to estimate the Center of Mass (CoM) position and the Tensor of Inertia (ToI) of the skipper. The system allows for a real-time computation of the inertial properties of the skipper, thus enabling a real-time update of the mass matrix of the numerical model running the human-in-the loop simulator. This study shows the results achievable by the system in terms of inertia estimation for different skipper’s poses, and compares the cases in which the skipper is modelled as a point mass with real-time position updates with the case in which the full inertia tensor of the skipper is considered. Results show how in contexts like those of Moth class sailboat simulators the possibility to take into account and to vary in real-time the tensor of inertia of the sailor increases the accuracy achievable in the dynamic simulation. © 2018 IEEE.

关键词： Cameras

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Assessment Technology for Physiotherapy Practices using Deep Learning 2

Assessment Technology for Physiotherapy Practices using Deep...

引用

2nd International Conference on Advancements in Smart, Secure and Intelligent Computing, ASSIC 2024

作者： Vineeth, Nandhini Darshan, P.M. Deepu, K. Daragaj, Arihant Prabhu, Ananth G. Bms College of Engineering Dept. of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798350370188

The innovative technology for physiotherapy assessment employing deep learning aims to offer a precise and streamlined approach to evaluating physiotherapy needs. Conventional manual assessment techniques utilized in physiotherapy often consume time and are susceptible to human inaccuracies, resulting in erroneous diagnoses and treatment strategies. This technology tackles these challenges by employing sophisticated deep learning algorithms that identify angles and offer auditory guidance to patients based on their posture. The process commences with capturing the patient's video through a webcam and extracting frames utilizing OpenCV. Following this, the frames undergo processing via a media pipe library, a sophisticated tool designed for body key point detection. These identified key points are subsequently utilized to connect essential body segments for a particular exercise and compute the angular measurements between them. Using these measurements, the system assesses the correctness of the posture, offering audio feedback with repetition counts for accurate postures and tailored guidance for incorrect ones. The audio cues are exercise-specific, delivering precise instructions to aid the patient in refining their posture. The system also keeps track of patient's progress over time and provides a visual display of their improvement. This helps the patient monitor their progress and provides motivation for them to continue with their therapy. The use of learning algorithms and the media pipe library makes this solution an accurate, efficient, and cost-effective method for physiotherapy assessments. © 2024 IEEE.

关键词： Deep learning

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Unleashing Machine Learning for Hepatitis C Prediction: A Holistic Exploration of Clinical Insights

Unleashing Machine Learning for Hepatitis C Prediction: A Ho...

引用

2024 IEEE International Conference on Computing, Applications and Systems, COMPAS 2024

作者： Al-Amain Janin, Fatema Tuj Ahmed Robin, Faizul Ahmed, Shifat Mohi Uddin, Khandaker Mohammad Southeast University Dept. of Computer Science and Engineering Dhaka Bangladesh Hamburg University of Applied Sciences Dept. of Information and Electrical Engineering Hamburg Germany

ISBN: (纸本)9798331529765

Chronic diseases, characterized by long-term effects and persistent symptoms, pose significant challenges to human health and well-being. The hepatitis C virus (HCV) is the cause of chronic hepatitis C, which affects 58 million people globally. It also causes 290,000 deaths annually due to liver cancer and cirrhosis, two conditions connected to HCV. Taking advantage of machine learning (ML) algorithms' ability to analyze large datasets and identify patterns and relationships that aid in diagnosis, this effort focuses on employing algorithms for machine learning to identify and forecast hepatitis C precisely. Various techniques were utilized, including Random Forest, Cat Boost, SVM, and others to create predictive models using blood values from 615 patients, including both hepatitis patients and healthy blood donors. The dataset underwent comprehensive preprocessing, including handling missing values, scaling, and dimensionality reduction via Principal Component Analysis (PCA). Various ML algorithms were evaluated, and the Cat Boost classifier achieved the highest accuracy of 99.17%, recall of 92.00%, precision of 100.00% and F1-score is 96.00%. This study demonstrates the potential of ML in enhancing HCV diagnosis, offering less invasive and cost-effective alternatives to traditional methods, and providing predictive insights for personalized treatment plans. The findings underscore the importance of advanced data preprocessing and feature selection in improving model efficiency. This study adds to the ongoing efforts to lessen the prevalence of hepatitis C worldwide, aligning with the World Health Organization's goal to significantly decrease new HCV cases and related deaths by 2030. © 2024 IEEE.

关键词： Adversarial machine learning

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Hybrid Image Encryption for IoT Applications: Integrating Cellular Automata and Henon Map to Improve Security and Performance 4

Hybrid Image Encryption for IoT Applications: Integrating Ce...

引用

4th International Conference on Technological Advancements in Computational sciences, ICTACS 2024

作者： Yogi, Biswarup Khan, Ajoy Kumar Roy, Satyabrata Mizoram University Dept. of Computer Engineering Mizoram Aizawl India Manipal University Dept. of Computer Science and Engineering Rajasthan Jaipur India

ISBN: (纸本)9798350387490

Securing the vast volumes of data exchanged over networks is crucial in IoT (Internet of Things) applications. This study introduces an innovative image encryption algorithm that combines the interactive properties of Cellular Automata (CA) and the Henon map to improve both security and performance. CA, renowned for its simplicity and parallelism, are utilized to produce intricate pseudorandom sequences. These sequences are then used as the keystream for encryption purposes. Whereas, the Henon map is employed to incorporate further levels of unpredictability and intricacy into the encryption procedure. The proposed method is thoroughly assessed through considerable experimentation and is compared with existing methodologies. The findings demonstrate substantial enhancements with respect to various standard metrics such as 'Mean Squared Error (MSE)'., 'Peak Signal-to-Noise Ratio (PSNR)'., 'Unified Average Changing Intensity (UACI)'., and 'Number of Pixels Change Rate (NPCR)' measurements., underscoring the resilience and efficacy of the proposed methodology. The results emphasize the capability of this hybrid method to offer a secure and robust performance for image encryption in IoT applications. This method guarantees the integrity and confidentiality of data while outperforming current encryption techniques in performance. © 2024 IEEE.

关键词： Mean square error

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Using the Wavelet Packet Transform to Evaluate Parameters of Harmonics Clustered in Quadruples Using Linear Systems

Using the Wavelet Packet Transform to Evaluate Parameters of...

引用

2024 IEEE International Symposium on Electromagnetic Compatibility, Signal and Power Integrity, EMC+SIPI 2024

作者： Nicolae, Ileana-Diana Nicolae, Petre - Marian Nicolae, Marian-Ştefan University of Craiova Dept. of Computer Science and It Craiova Romania University of Craiova Dept. of Electrical Engineering Energetic and Aeronautics Craiova Romania

ISBN: (纸本)9798350360394

The paper presents an original technique used to evaluate the parameters of quadruples of harmonics clustered in an almost exclusive manner to quadruples of nodes from the bottom level of a Wavelet Packet Tree. The harmonic signal (RHS) generated by harmonics from a quadruple can be deduced with approximation from the analyzed signal by using original computational techniques. Linear systems were conceived based on the harmonics superposition and randomly chosen components of RHC (the approximated version of RHS). Their solving yields sets of possible solutions, that are afterward refined to exclude values with non-zero imaginary parts or deviant values. Averages over refined values are used. The indetermination relative to phase-shifts is solved considering the criterion of "best approximation"of RHC by the signal obtained through the superposition of the computed clustered harmonics. The harmonics from the studied quadruples are: (11, 13, 19, 21), (23,25,39,41) and (27,29,35,37). This paper provides the complementary technique for the odd harmonic identification within the range 3...41 (the odd harmonics not belonging to quadruples are coupled in pairs and were approached in a previous publication). © 2024 IEEE.

关键词： Wavelet analysis

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Enhanced Fetal Ultrasound Image Segmentation using Spatial Attention Mechanisms with UNet: SAUnet

Enhanced Fetal Ultrasound Image Segmentation using Spatial A...

引用

2024 IEEE International Conference on computer Vision and Machine Intelligence, CVMI 2024

作者： Verma, Harshita Patro, Bdk Rajkiya Engineering College Dept. of Computer Science and Engineering Kannauj India

ISBN: (纸本)9798350376876

Fetal Ultrasound (US) imaging is necessary to prenatal treatment, as it provides information on the health and development of the fetus. Accurate segmenting is essential but poses significant challenges due to the dynamic fetal anatomy. This paper proposes Unet with Spatial Attention (SAUnet), a novel deep learning architecture that integrates attention mechanisms into the Unet framework. Unlike traditional models, SAUnet dynamically emphasizes key anatomical structures while suppressing irrelevant background information, enhancing the model's focus on informative regions within the US images. This targeted approach leads to improved segmentation accuracy and robustness. Our method was evaluated on the HC18 Challenge dataset, achieving a mean absolute difference(±std) of 2.23 ± 2.41 mm in head circumference measure and a mean Dice similarity coefficient of 97.63 ± 1.85%, demonstrating high segmentation accuracy. Additionally, mean difference and Hausdorff distance are 1.06 ± 3.11 mm and 1.42 ± 0.93 mm respectively suggesting robust performance. Incorporation of spatial attention in Unet architecture marks a significant advancement in fetal US image segmentation, offering a lightweight and efficient model suitable for real-time clinical deployment. Benchmark comparisons highlight SAUnet's competitive performance, reinforcing its potential as a state-of-the-art solution for prenatal imaging tasks. © 2024 IEEE.

关键词： Semantic Segmentation

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Load Flow Analysis and Optimization for Solar PV Integration in Power Systems 21

Load Flow Analysis and Optimization for Solar PV Integration...

引用

21st International Learning and Technology Conference, L and T 2024

作者： Shigdar, Basmah Binsalim, Haya Almatrafi, Lina Badaam, Salma Barkat, Enfel Effat University Dept. of Electrical & Computer Engineering Jeddah Saudi Arabia

ISBN: (纸本)9798350393569

Saudi Arabia's Vision 2030 emphasizes renewable energy to drive sustainable development, particularly leveraging solar power due to abundant sunshine. However, integrating solar photovoltaic (PV) systems into existing grids presents multifaceted challenges. Voltage regulation, power quality issues, and reverse power flow disruption due to solar intermittency demand effective management strategies. Challenges, including voltage fluctuations, power quality issues, reverse power flow disruptions, and solutions like energy storage, advanced inverter control, demand response, and grid modernization, are explored to mitigate these challenges. A literature review and methodologies are presented, including PV generator modeling and power flow calculations. The proposed methodology is validated through case studies and a simulation based on the Institute of electrical and Electronics Engineers (IEEE) 30-Bus System, showcasing differences in voltage and angle outputs across nodes with integrated PV generators. © 2024 IEEE.

关键词： Power quality

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