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2024 1st International Conference on Logistics (ICL)

作者： Salama A. Mostafa Wisam Subhi Al-Dayyeni Ali Noori Kareem Mohammed Ahmed Jubair Mustafa Musa Jaber Bashar Ahmad Khalaf Faculty of Computer Science and Information Technology Universiti Tun Hussein Onn Malaysia Johor Malaysia School of Information Technologies and Engineering ADA University Baku Azerbaijan Computer Engineering Department Bilad Alrafidain University College Ba'aqubah Diyala Iraq Department of Computer Technical Engineering College of Information Technology Imam Ja'afar Al-Sadiq University Al-Muthanna Iraq Department of Medical Instruments Engineering Techniques Al-Farahidi University Baghdad Iraq Department of Medical Instruments Engineering Techniques Bilad Alrafidain University College Diyala Iraq

ISBN: (数字)9798331518844

ISBN: (纸本)9798331518851

This study is about developing sentiment analysis and classification method for Amazon Alexa products based on the rating and feedback given by customers. The purpose of the method is to investigate the polarity of positive or negative opinions expressed by the buyers of the products to attain insights and recommendations from the provided reviews by the customers. The Amazon Alexa Reviews dataset used in this work consists of 3150 reviews. It contains features related to the ratings, dates, variants, and customer comments on several Amazon Alexa products, such as Echo Dots and Alexa Echo. Four classification algorithms of Decision Tree (DT), Random Forest (RF), XgBoost (XB), and k-Nearest Neighbors (k-NN) are implemented, and the evaluation metrics of accuracy, precision, and recall compare their performances. The evaluation results of the four algorithms have revealed that the RF achieves the best performance among the four classifiers, with an average accuracy score of 94.17%, a precision score of 93.71%, and a recall score of 99.33 %.

关键词： Radio frequency Measurement Sentiment analysis Accuracy Reviews Virtual assistants Nearest neighbor methods Classification algorithms Random forests Logistics

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Limited quantum advantage for stellar interferometry via continuous-variable teleportation

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Physical Review A 2024年第5期109卷 052434-052434页

作者： Zixin Huang Ben Q. Baragiola Nicolas C. Menicucci Mark M. Wilde School of Mathematical and Physical Sciences Macquarie University NSW 2109 Australia Centre for Quantum Software and Information Faculty of Engineering and IT University of Technology Sydney NSW 2007 Australia Centre for Quantum Computation and Communication Technology School of Science RMIT University Melbourne VIC 3000 Australia School of Electrical and Computer Engineering Cornell University Ithaca New York 14850 USA

We consider stellar interferometry in the continuous-variable (CV) quantum information formalism and use the quantum Fisher information (QFI) to characterize the performance of three key strategies: direct interferometry (DI), local heterodyne measurement, and a CV teleportation-based strategy. In the lossless regime, we show that a squeezing parameter of r≈2 (18 dB) is required to reach ∼95% of the QFI achievable with DI; such a squeezing level is beyond what has been achieved experimentally. In the low-loss regime, the CV teleportation strategy becomes inferior to DI, and the performance gap widens as loss increases. Curiously, in the high-loss regime, a small region of loss exists where the CV teleportation strategy slightly outperforms both DI and local heterodyne, representing a transition in the optimal strategy. We describe this advantage as limited because it occurs for a small region of loss, and the magnitude of the advantage is also small. We argue that practical difficulties further impede achieving any quantum advantage, limiting the merits of a CV teleportation-based strategy for stellar interferometry.

关键词： Quantum metrology Quantum parameter estimation Quantum teleportation

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Nodal Transmission in 5G Wireless Networking

Nodal Transmission in 5G Wireless Networking

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International Conference on Power Energy, Environment and Intelligent Control (PEEIC)

作者： Trapty Agrawal Pallabi Kakati Sunanda Das Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Department of Electronics and Communication Engineering Presidency University Bangalore Karnataka India Department of Computer Science Engineering Faculty of Engineering and Technology JAIN (Deemed-to-be University) Bangalore Karnataka India

The increase in data transmission network speed and reliability is inextricably linked to the fast growth of information technology. This calls for the ongoing improvement and upgrading of current standards. Next-generation communication networks are being developed and deployed with diligence by several organizations. This article provides a thorough examination of the software options available that are designed for analyzing and evaluating data network behaviors. It specifically emphasizes tools with substantial built-in functionality and flexibility that are ideally suited for mobile communication systems. It becomes clear that the NS3 network simulator is the best option. This article provides an overview as well as a unique NS3 solution that makes it possible to estimate the dependability of data transmission from a network node's first movement to its removal from a base station.

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Utilizing Cutting-Edge Machine Vision Techniques for Swift Anomaly Detection in Suboptimal Surveillance Environments

Utilizing Cutting-Edge Machine Vision Techniques for Swift A...

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International Conference on ICT in Business Industry & Government (ICTBIG)

作者： Rakesh Kumar Yadav Veena C S Gadug Sudhamsu Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Department of Electronics and Communication Engineering Presidency University Bangalore Karnataka India Department of Computer Science Engineering Faculty of Engineering and Technology JAIN (Deemed-to-be University) Bangalore Karnataka India

In the realm of surveillance and anomaly detection, the proposed Adaptive Multimodal Anomaly Detection (AMAD) method stands as an innovative and potent approach. The objective of this method is to detect anomalies swiftly and accurately in suboptimal surveillance environments by amalgamating state-of-the-art machine vision techniques with multimodal data fusion. The core idea revolves around leveraging advanced deep learning architectures and statistical models, thereby enhancing the performance of anomaly detection systems. The AMAD method initiates with Algorithm 1: Multimodal Data Fusion, a process that involves integrating multimodal data streams, such as visual, thermal, and infrared information, into a unified feature space. This integration is accomplished using a weighted sum approach, allowing varying importance levels to be assigned to each modality based on weights $(\alpha, \beta, \gamma)$ . This fusion strategy proves pivotal in capturing a holistic representation of the environment. Convolutional Neural Network (CNN) for Feature Learning follows, where a CNN architecture is employed to learn robust and discriminative features from the fused data. This study showcases that AMAD, with its multimodal integration, deep learning-based feature extraction, and efficient scoring mechanism, triumphs over traditional anomaly detection techniques. It significantly contributes to enhanced anomaly detection capabilities, emphasizing its potential for bolstering public safety and security in dynamic and challenging technological landscapes.

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Simulation Analysis of Single Input Multiple Output Buck Converter

Simulation Analysis of Single Input Multiple Output Buck Con...

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Communication and information technology (AICCIT), Al-Sadiq International Conference on

作者： Z. W. Zulkifli N. Redzuan Z. Muhammad W. A. Mustafa Ahmed Alkhayyat Nejood F. Abdulsattar Faculty of Electrical Engineering and Technology Universiti Malaysia Perlis Perlis Malaysia Department of Computer Technical Engineering College of Technical Engineering The Islamic University Najaf Iraq Department of Computer Technical Engineering College of Information Technology Imam Ja’afar Al-Sadiq University Al-Muthanna Iraq

This paper presents a modified buck converter with multiple output circuit and its analysis. This Single Input Multiple Output (SIMO) buck converter has attracted researcher heart as this simple circuit can be harness its benefits especially for those that love outdoor activities as this circuit can charge multiple devices with specific current demand. With the proposed topology, the circuit can promise to constant charge using two separated load or USB devices. However, some study needs to be made as to understand the behavior of the circuit completely in terms of the component selection and the impact of faulty selection of duty cycle control. This study is simulated using PSIM software. The results of the analysis are presented in a proper comparison for a better understanding of the operation.

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Managing Cloud Networks: A Classification of SDN-Enabled Clouds

Managing Cloud Networks: A Classification of SDN-Enabled Clo...

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Advances in Computation, Communication and information technology (ICAICCIT), International Conference on

作者： Vaishali Singh Kehkeshan Jallal S Sunanda Das Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Department of Electronics and Communication Engineering Presidency University Bangalore Karnataka India Department of Computer Science Engineering Faculty of Engineering and Technology JAIN (Deemed-to-be University) Bangalore Karnataka India

The idea of separating the management plane from the data-forwarding gear was first introduced by Software-Defined Networking (SDN), which has brought about a revolutionary new age in networking. This innovation makes networks programmable, flexible, and dynamically reconfigurable. These characteristics have great potential for the field of computer science in the cloud, where dynamic adjustments and reconfiguration are essential owing to on-demand consumption patterns. The many simulation and empirical assessment techniques that have been created expressly for SDN-enabled cloud settings are also highlighted by us. Finally, study conducted perform a critical review of the state of the research, highlighting gaps and suggesting possibilities for further study.

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Systematic Review: Predictive Models for the Winning Team of Super Leagues (SL) 2

Systematic Review: Predictive Models for the Winning Team of...

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2nd International Conference on Business Analytics for technology and Security, ICBATS 2023

作者： Muneer, Salman Alvi, Muhammad Bux Al Sakhnani, Mahmoud Raza, Hamamd Ghazal, Taher M. Ahmad, Munir NCBA&E School of Computer Science Lahore Pakistan Applied Science Private University Applied Science Research Center Amman11937 Jordan The Islamia University of Bahawalpur Department of Computer Systems Engineering Bahawalpur Pakistan University City Sharjah School of Business Skyline University College Sharjah1797 United Arab Emirates National College of Business Administration & Economics School of Computer Science Lahore Pakistan Center for Cyber Security Faculty of Information Science and Technology Bangi43600 Malaysia School of Information Technology Skyline University College University City Sharjah 1797 United Arab Emirates

ISBN: (纸本)9798350335644

The Pakistan Super League (PSL) is one of the most popular cricket leagues in the world, attracting millions of fans and spectators each year. With the 2023 edition of the league just around the corner, the use of predictive models to determine the winning team has gained much attention. This systematic review analyzed the existing literature on predictive models for determining the winning team of PSL 2023. The study examined multiple approaches, including machine learning, statistical modeling, and expert opinion. The results of this review suggest that machine learning models, particularly ensemble models, are the most accurate predictors of PSL match outcomes. These models typically use a combination of features, such as team rankings, player performance statistics, match-winning ratio, and previous match outcomes, to predict the result of a match. This paper discovered that expert opinion-based models could provide valuable insights into the likely winner of a match. These models typically use input from cricket experts and analysts to predict the outcome of a match. © 2023 IEEE.

关键词： Machine learning

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Efficient Segmentation for wsn Connectivity Technologies

Efficient Segmentation for wsn Connectivity Technologies

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International Conference on Power Energy, Environment and Intelligent Control (PEEIC)

作者： Vivek V Savita Aruna Dore Department of Computer Science Engineering Faculty of Engineering and Technology JAIN (Deemed-to-be University) Bangalore Karnataka India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Department of Electronics and Communication Engineering Presidency University Bangalore Karnataka India

In wireless sensors networks, the chargeable capacity of sensor nodes serves as the primary energy source. In WSNs, grouping has emerged as a crucial strategy to save energy use and lengthen network lifespan. Transport energy is inversely correlated to the amount of distance across transmitter and destination nodes, which supports clustering. In this paper, we provide a novel fuzzy logic model for the cluster head (CH) choice, a key step in clustering in WSNs. The recommended model includes five characteristics to assess each node's suitability for CH status. The closeness to the starting place, volume, topographical suitability, and remaining power are some of these criteria. This fuzzy logic methodology is used to propose the Fuzzy Reasoning-based Energy-Efficient Grouping for WSN, with emphasis on establishing the shortest possible distance across CHs (FL-EEC/D). As a further metric of clustered methods' energy use, we also evaluate how efficiently they distribute energy among the sensors inside the WSN using the Gini index. We compare our proposed FL-EEC/D approach with the Low Energy Adaptation Clustered Architecture (LEACH), a fuzzy logic-based clustering approach, and various techniques. For various network sizes and topologies, simulation findings show considerable improvements in energy efficiency, network longevity, and balanced energy consumption across sensor nodes. Our results show that initial node depletion and half node depletion have significantly improved on average.

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Using Augmented Reality to Teach Primary Students Time Concepts: Development and Evaluation of AR Clock 6

Using Augmented Reality to Teach Primary Students Time Conce...

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6th International Conference on Informatics, Multimedia, Cyber and information System, ICIMCIS 2024

作者： Setyadinsa, Radinal Pendit, Ulka Chandini Kamsin, Afifah Dolhait, Lutfi University of Pembangunan Nasional 'Veteran' Jakarta Faculty of Computer Science Jakarta Indonesia College of Business Technology and Engineering Sheffield Hallam University Sheffield United Kingdom Content Tree Malaysia University of Teknikal Malaysia Melaka Faculty of Information and Communication Technology Melaka Malaysia

ISBN: (纸本)9798331533243

This study presents the development and evaluation of AR Clock, a mobile Augmented Reality (AR) application designed to teach time concepts to primary school students. The application addresses the growing concern about children's limited understanding of time and their difficulty in reading analogue clocks. AR Clock was developed using Agile methodology, Unity, and Vuforia SDK to create an interactive and engaging learning experience. The application features multiple levels of time-telling exercises, from basic hour reading to complex time concepts, complemented by a quiz feature for self-assessment. A user study was conducted with 25 primary school students (ages 5-6) and one mathematics teacher to evaluate the effectiveness of AR Clock showed positive outcomes that indicated AR Clock effectively combines AR technology with educational content to create an engaging learning tool for time concepts. The application's strengths lie in its intuitive interface, interactive features, and the use of AR to enhance the learning experience. This study contributes to the growing body of research on AR applications in education, particularly in teaching fundamental mathematical concepts to young learners. © 2024 IEEE.

关键词： Students

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Data-efficient large vision models through sequential autoregression 24

Data-efficient large vision models through sequential autore...

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Proceedings of the 41st International Conference on Machine Learning

作者： Zhiwei Hao Jianyuan Guo Chengcheng Wang Yehui Tang Han Wu Han Hu Kai Han Chang Xu School of information and Electronics Beijing Institute of Technology Beijing China School of Computer Science Faculty of Engineering University of Sydney Sydney Australia Huawei Noah's Ark Lab Beijing China

Training general-purpose vision models on purely sequential visual data, eschewing linguistic inputs, has heralded a new frontier in visual understanding. These models are intended to not only comprehend but also seamlessly transit to out-of-domain tasks. However, current endeavors are hamstrung by an over-reliance on colossal models, exemplified by models with upwards of 3B parameters, and the necessity for an extensive corpus of visual data, often comprising a staggering 400B tokens (Bai et al., 2023). In this paper, we delve into the development of an efficient, autoregression-based vision model, innovatively architected to operate on a limited dataset. We meticulously demonstrate how this model achieves proficiency in a spectrum of visual tasks spanning both high-level and low-level semantic understanding during the testing phase. Our empirical evaluations underscore the model's agility in adapting to various tasks, heralding a significant reduction in the parameter footprint, and a marked decrease in training data requirements, thereby paving the way for more sustainable and accessible advancements in the field of generalist vision models. The code is available at https://***/ggjy/DeLVM.

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