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A Machine-Learning Ensemble Method for Temporal-aware Sales Forecasting 5

A Machine-Learning Ensemble Method for Temporal-aware Sales ...

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5th international conference on data Science and Information technology, DSIT 2022

作者： Aun, Yichiet Khaw, Yen-Min Jasmina Gan, Ming Lee Chern, Debra Lee Hooi Universiti Tunku Abdul Rahman Faculty of Information Communication and Technology Malaysia

ISBN: (数字)9781665498685

ISBN: (纸本)9781665498685

Machine-learning-driven sales forecasting is gaining momentum in inventory management based on customer shopping behaviors. However, it is difficult to build a one-size-fits-all sales forecasting model for general purpose use due to the volatility of product turnaround and complex consumer behaviors. Traditionally, single forecasting models such as ARIMA modeling are used to predict sales. However, existing models have little real- world applicability given the apparent forecast disparity between experimental and real-world predictions due to model overfitting. this project proposes an ensemble Machine-learning method using weighted XGBoost, ARIMA, and Holt-Winters for sales forecasting of a grocery chain to address model overfitting issues. the intuition of the work is to combine several ML models to reduce variance (bagging), bias (boosting), and improve predictions (stacking). the contribution of this work includes: (1) implementation of exploratory modeling for in-depth exploratory data analysis (EDA) in grocery datasets and (2) feature engineering for the weighted integration of designed forecasting models using a lag and sliding window techniques. the preliminary result of the research places the model developed at the 70th percentile of the Corporacion Favorita Sales Forecasting Competition in Kaggle. © 2022 IEEE.

关键词： big data

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How AI Algorithms Are Being Used in Applications 1

引用

5th international conference on Soft Computing and Signal Processing, ICSCSP 2022

作者： Mohammed, Aleem Mohammad, Mohammad Computer Science SydneyNSW Australia Department of Information Technology Melbourne Institute of Technology SydneyNSW Australia

ISBN: (数字)9789811986697

ISBN: (纸本)9789811986680

Artificial intelligence, known as AI, is slowly turning more common in a variety of engineering sectors. Artificial intelligence is finding applications in a variety of fields, including medicine, architecture and construction, sports, entertainment, and gaming. In a world where data is being generated at a rate of many terabytes per second, algorithms that can accurately predict the impacts of events based on large data sets will become useful in moving the financial system forward, according to the Second international conference on data Science and technology. In this article, we defined the application of artificial intelligence and system mastering in order to identify its usefulness across a wide range of fields. the potential of artificial intelligence lies in its ability to forecast outcomes with high accuracy, but this requires more data. Ideas like deep mastery and recurrent neural networks give the inspiration for the development of powerful artificial intelligence programs. © 2023, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Sports

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Cooperative Non-Orthogonal Multiple Access for B5G Wireless Networks: Modeling and Simulations 10

Cooperative Non-Orthogonal Multiple Access for B5G Wireless ...

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10th international conference on Wireless Networks and Mobile Communications, WINCOM 2023

作者： Ahmed, Ridha Alaa Sadia, Haleema University of Kufa Department of Electronics and Communication Engineering Najaf Iraq Ghulam Ishaq Khan Institute of Engineering and Technology Faculty of Electrical Engineering Topi Pakistan

ISBN: (纸本)9798350329674

Beyond 5th-Generation (B5G) systems require massive capacity with low-latency and ubiquitous connectivity. Non-Orthogonal Multiple Access (NOMA) is a potential scheme to meet this requirement by enabling efficient spectrum utilization and user multiplexing. Among the NOMA integration techniques, Cooperative NOMA (C-NOMA) is an innovative technique that can enhance the transmission rate, edge user performance, and spectral efficiency. However, conventional C-NOMA systems have challenges in optimizing user performance. In this article, we address this problem in a downlink C-NOMA system model by employing the Space-Time Block Codes (STBC) approach in a two phase transmission scenario. In the first phase, the NOMA signal is broadcasted by the base station to all users, while in the second phase, the two near users cooperate using the Alamouti code scheme to relay the far user's signal. We obtain closed-form expressions for the bit error rate for both phases and compare the proposed scheme with the conventional C-NOMA. We use MATLAB to implement the models and analyze the performance of the proposed system in terms of symbol error rate evaluations. the results indicate that STBC-C-NOMA achieves a 2.1 dB gain over conventional C-NOMA. this work provides valuable insights for future research in related C-NOMA fields. © 2023 IEEE.

关键词： Bit error rate

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An Extensive Study: Creation of A New Inverted Microscope Image data Set and Improving Auto-Encoder Models for Higher Accuracy Segmentation of HaCaT Cell Culture Line 5

An Extensive Study: Creation of A New Inverted Microscope Im...

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5th international Congress on Human-Computer Interaction, Optimization and Robotic Applications, HORA 2023

作者： Sevim, Zeynep Dogan, Hulya Demir, Zeynep Sezen, Feride Sena Dogan, Ramazan Ozgur Gumushane University Department of Artificial Intelligence and Intelligent Systems Gumushane Turkey Karadeniz Technical University Faculty of Engineering Department of Software Engineering Trabzon Turkey Karadeniz Technical University Drug and Pharmaceutical Technology Application & Research Center Trabzon Turkey Karadeniz Technical University Faculty of Pharmacy Department of Pharmacology Trabzon Turkey Gumushane University Faculty of Engineering and Natural Sciences Department of Software Engineering Trabzon Turkey

ISBN: (纸本)9798350337525

In cell culture investigations, it is assumed that the number of cells is counted automatically without the need for forward planning, additional tools, or consumables. the automatic cell counting process is greatly facilitated and made functional by the segmentation of cell culture lines. Many studies with high-accuracy results have been developed for cell or tissue segmentation in images acquired from microscopic systems, but this success cannot be achieved for segmentation of cell culture line (especially in images acquired from inverted microscope). there are still restrictions in segmentation of cell culture line studies in inverted microscope systems. In this context, a new inverted microscope image data set is created and higher accuracy auto-encoder models are developed for segmentation of HaCat cell culture line. the analyzes of the developed auto-encoder models are evaluated using four different performance evaluation procedures which are Accuracy, Sensitivity, Specificity and Jaccard. In terms of the performance evaluation procedures used in this study, the achievements of the auto-encoder models are clearly satisfactory for the automated segmentation of the HaCat cell culture line. © 2023 IEEE.

关键词： Microscopes

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Substation smoking behavior detection based on improved decoupling head 2

Substation smoking behavior detection based on improved deco...

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2nd international conference on Electronic Information engineering, big data, and Computer technology, EIBDCT 2023

作者： Zhang, Shuai Liang, Shuaiwei Mei, Bin-Xiao Li, Ding Rui, Han Hangzhou Yineng Electric Technology Co. LTD Hangzhou310000 China State Grid Zhejiang Electric Power Co. LTD Hangzhou310000 China State Grid Zhejiang Electric Power Co. LTD. Research Institute Hangzhou310000 China Zhejiang University of Science and Technology Hangzhou310023 China

ISBN: (纸本)9781510664968

In the complex environment of the substation, small objects such as long-distance cigarettes, cigarette boxes, and lighters have few imaging pixels and lack texture information, making it difficult for convolutional neural networks to extract small object features. In the case of multiple targets, the missed detection rate and false detection rate of small targets are high, and the fusion of model features is insufficient, making it difficult to accurately identify and detect. Aiming at the above problems, a multi-scale small target detection algorithm is proposed. In the prediction part of the network, a more effective decoupling head is designed. In addition, shallow features are introduced to improve the feature pyramid, extract small target features, increase the correlation between multiple targets, and prevent the loss of small target feature information. At the same time, a multi-layer attention mechanism is embedded in the backbone network to increase the regional features of invisible small targets and reduce the missed detection rate. In the post-processing stage, the Focal Loss loss function is introduced to increase the model's learning of positive sample targets and further reduce the rate of missed detection and false detection. the experimental results show that the method achieves a mAP@. 5:.95 of 0.6350 on the homemade smoking dataset, and mAP@0.5 achieves 0.9569. For the self-made multi-scene and multi-scale smoking data set, this model has advantages in detection accuracy compared with the current excellent target detection models such as YOLOX and YOLOv5. the experimental results show that the model method can realize the identification and detection of small targets such as cigarettes and lighters under multi-scale targets, which has a certain reference value for anti-smoking measures. © 2023 SPIE.

关键词： Deep learning

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Characterizing the Weed Species of Agricultural Landscapes Using Hyper Spectral Image Analysis 5th

Characterizing the Weed Species of Agricultural Landscapes U...

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5th international conference on data Science, Machine Learning and Applications, ICDSMLA 2023

作者： Nagaraja, S.R. Mishra, Awakash Prabhu, A. Sivaraman, Himani School of Computer Science and Engineering Presidency University Karnataka Bengaluru India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India Karnataka Bangalore India Department of Computer Science and Engineering School of Engineering and Computing Dev Bhoomi Uttarakhand University Manduwala Dehradun Navgaon India

ISBN: (纸本)9789819780426

this paper explores using hyper spectral photo evaluation to discover and characterize weed species inside agricultural landscapes. By using excessive-decision hyperspectral imaging, the specific spectral fingerprints of the different weed species attempting to penetrate the crop canopy can be diagnosed. this fact can then be compared to spectral libraries for the precise identity of the weed species gift. With these spectral facts, researchers can map the spread of weeds, understand the composition of weed species present, and verify weed responses to exceptional farming strategies or weather changes. this information can then assist in the development of extra effective and selective weed management strategies in agriculture. In conclusion, hyperspectral picture analysis may provide a treasured tool for characterizing and dealing with weed species within agricultural landscapes. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： characterizing hyperspectral weather

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A Proactive Approach for Behavior Based Ransomware Detection 5

A Proactive Approach for Behavior Based Ransomware Detection

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5th international conference on Advancements in Computing, ICAC 2023

作者： Madanayaka, B.P.W. Dias, N.S.A. Samaranayake, A.K.D.D.V. Karawita, K.V.D.A.U. Abewardhana, Kavinga Yapa Siriwardana, Deemantha Sri Lanaka Institute of Information Technology Faculty of Computing Malabe Sri Lanka

ISBN: (纸本)9798350358131

Ransomware attacks pose a pervasive threat, demanding robust and proactive defense mechanisms. Traditional signature-based detection methods fall short in keeping pace with the evolving ransomware landscape. To tackle this issue, researchers have introduced a behavior-based ransomware detection tool. this tool employs various strategies, such as monitoring suspicious processes and behaviors, analyzing power consumption and CPU temperature, measuring changes in stored data entropy, and incorporating a honeypot environment. By effectively distinguishing between legitimate and ransomware-related activities, the tool takes a proactive approach to identify unknown or evolving ransomware strains. Continuous monitoring of power consumption and CPU temperature ensures precise detection of unusual encryption activities. Furthermore, entropy analysis adds an additional layer of detection by pinpointing alterations in stored data that may signal ransomware. the honeypot environment acts as a valuable resource for analyzing new ransomware variants and enhancing detection algorithms. this paper offers a comprehensive overview of the behavior-based ransomware detection tool, including its technical architecture, evaluation methods, and experimental results. the tool has demonstrated the ability to effectively detect ransomware attacks, safeguarding critical systems. It also suggests opportunities for further research and improvement in ransomware detection techniques. © 2023 IEEE.

关键词： Entropy

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Dynamic transitions of functional brain connectivity during a working memory task

Dynamic transitions of functional brain connectivity during ...

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45th Annual international conference of the IEEE-engineering-in-Medicine-and-Biology-Society (EMBC)

作者： Yang, Yunseon Yoo, Seulki Park, Bo-yong Inha Univ Artificial Intelligence Convergence Res Ctr Incheon South Korea Inha Univ Dept Data Sci Incheon South Korea Inst Basic Sci Ctr Neurosci Imaging Res Suwon South Korea

ISBN: (纸本)9798350324471

Working memory is a cognitive system that temporarily stores and manipulates information via attention. Although brain activity patterns related to working memory have been extensively studied, little is known about how the brain connectome organization dynamically changes while performing working memory tasks. Here, we systematically investigated dynamic changes in functional brain connectivity during a working memory task. We found that functional connectivity in the medial frontal and orbitofrontal cortices and the precuneus showed differences during the transitions between the rest and task. Our results provide topographic patterns of dynamic functional connectivity during the working memory tasks.

关键词： Topography

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Robustness of k-Anonymization Model in Compliance with General data Protection Regulation 5

Robustness of k-Anonymization Model in Compliance with Gener...

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5th international conference on Computing and big data, ICCBD 2022

作者： Abubakar, Ibrahim Bio Yagnik, Tarjana Mohammed, Kabiru Engineering and Media de Montfort University Department of Computing Leicester United Kingdom

ISBN: (纸本)9781665457163

the advancement in technology and the emergence of big data and the internet of things (IoT), individuals (data subjects) tend to suffer from privacy breach of various types that has led to a lot of damages to both data subjects and brands. these and other issues about data privacy breach led the European Union to come up with a much stringent regulations that will serve as a deterrent to businesses or organizations that handle data. this gave birth to the General data Protection Regulation (GDPR) in 2018 which replaced the previous 1995 data Protection Directive in Europe. this research examined the robustness of k-anonymity in compliance with GDPR regulations at varying k-values (5,10,50, and 100) using the 1994 USA Census Bureau data referred to as the adult dataset. Various measures were used to determine which k-value meets the GDPR criteria and the findings revealed the best anonymizing threshold complies with the GDPR criteria that prevents information loss (which determines data utility), prosecutor re-identification risk percentage and attacker models (prosecutor, journalist and marketer model). © 2022 IEEE.

关键词： data privacy

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Enhancing SVM Classification of Meningitis with Feature-Adaptive Adagrad in CSF Analysis 5

Enhancing SVM Classification of Meningitis with Feature-Adap...

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5th international conference on Electronics and Sustainable Communication Systems, ICESC 2024

作者： Sathiya Priya, S. Amanullah, M. Manivannan, D. Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Department of Computer Science & Engineering Chennai India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Computer Science & Engineering Chennai600062 India

ISBN: (纸本)9798350379945

Meningitis, a condition characterized by a serious inflammation of the meninges, necessitates timely diagnosis to get the best possible results for the patient. An examination of cerebrospinal fluid (CSF) continues to be an essential method for detecting meningitis. the study examines the capacity of Support Vector Machines (SVM) enhanced with the Adagrad algorithm to enhance the precision of meningitis classification by using CSF data. this study investigates the effectiveness of Adagrad in improving the performance of Support Vector Machines (SVM) for predicting meningitis. Two Support Vector Machine (SVM) models were created in this study to examine the effects of optimization strategies. the objective of this comparison was to assess if the use of Adagrad might enhance the precision of the Support Vector Machine (SVM) in a research study. Both models are trained using a dataset that includes labeled CSF analysis findings, which are classed based on whether meningitis is present or absent. the assessment of the testing dataset and training dataset was conducted using measures such as accuracy, sensitivity (the ability to properly detect meningitis), and specificity (the ability to accurately identify non-meningitis). though Support Vector Machine (SVMs) is a well-established tool for performing classification tasks like meningitis prediction, this research explores how the Adagrad algorithm can optimize SVM performance. By dynamically adjusting learning rates, Adagrad can potentially help the SVM converge faster to a better solution, leading to more accurate meningitis diagnosis from CSF analysis. the effectiveness of this approach depends on the quality and relevance of the features extracted from the CSF analysis. Choosing informative features is crucial for both SVM and Adagrad to perform well. the influence of Adagrad optimization on the model's capacity to reliably identify meningitis based on CSF variables may be examined by comparing the results. Healthcar

关键词： Support vector machines

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