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An IoT Solution for Enhanced Tray Management Systems in Food Service and Industrial Settings

An IoT Solution for Enhanced Tray Management Systems in Food...

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2024 IEEE International Conference on Signal Processing and Advance Research in Computing, SPARC 2024

作者： Chaudhary, Vipin Kumar Singh, Suchita Singh, Nagendra Pratap Afreen, Sana Yadav, Manmohan Singh Gupta, Durgesh Kumar Lovely Professional University Dept. of Computer Science and Engineering Punjab Phagwara India Buddha Institute of Technology Dept. of Computer Science and Engineering Uttar Pradesh Gorakhpur India Dr. B R Ambedkar National Institute of Technology Dept. of Computer Science and Engineering Punjab Janlandhar India Telangana Hyderabad India Sharda University Dept. of Computer Science and Engineering Uttar Pradesh Greater Noida India Institute of Engineering and Technology Dept. of Computer Science and Engineering Uttar Pradesh Sitapur India

ISBN: (纸本)9798350385199

This study introduces TraySmart, a cutting-edge Internet of Things (IoT) tool meant to completely transform the effectiveness and administration of tray systems in industrial, food court, and cafeteria environments. TraySmart employs cutting-edge technologies like weight sensing, RFID tag reading, temperature monitoring, NFC for user interaction, and Wi-Fi Bluetooth for data transmission to tackle the widespread issues associated with tray management, such as inefficient operations, concerns about food safety, and less-than-ideal user experiences. The weight-sensing feature of the device makes it possible to identify objects on the tray and monitor the contents of the tray in real time. Reading RFID tags makes identifying and tracking items easier, improving inventory control and lowering loss. Food safety and quality assurance depend on food items maintaining their ideal temperatures, which is ensured via temperature monitoring. Furthermore, NFC technology enhances the eating experience by enabling users to engage with the smartphone for payments or feedback. The communication options on the device guarantee smooth data transfer to a central system, facilitating thorough data analysis and system optimization. This essay covers the creativity, design, and development of the TraySmart prototype, the difficulties faced, and the strategies used in the process. The report assesses TraySmart's effect on operational efficiency, food safety standards, and customer happiness by carefully examining the outcomes of pilot testing. With its scalable solution to improve tray management systems, TraySmart marks a significant leap in the application of IoT technology in the food service and industrial sectors. The results highlight how IoT gadgets like TraySmart have the power to revolutionize operational procedures and service delivery, establishing a new benchmark for innovation in the industry. © 2024 IEEE.

关键词： Customer satisfaction

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Enhanced Detection of DoS/DDoS Attacks in SDN Using Ensemble and Hybrid CNN-LSTM Models

Enhanced Detection of DoS/DDoS Attacks in SDN Using Ensemble...

引用

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

作者： Afroj, Maharin Rifat, Khan Md Sadbin Rahman, Md. Saifur Bangladesh University of Business and Technology Dept. of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9798331529765

Software-Defined Networking (SDN) faces significant challenges in mitigating Attacks known as denial of service (DoS) and distributed DDoS due to its centralized control plane architecture. This centralization makes the control plane an attractive target for attackers, highlighting the need for effective detection and mitigation strategies. In this study, we propose three innovative methodologies to enhance the accuracy of detecting DoS/DDoS attacks in SDN environments. Our first methodology employs an ensemble model with a voting classifier, which achieves a detection accuracy of 97.4%. This approach leverages multiple weak learners to enhance the robustness of detection. The second methodology utilizes an ensemble with a stacking technique, attaining a superior accuracy of 99%, demonstrating the efficacy of integrating multiple base models in a hierarchical fashion. The third approach introduces a novel hybrid model that blends LSTM networks with CNN, achieving a competitive accuracy of 98.51%. A comparative analysis of these methods reveals that, while all three significantly enhance detection rates, the stacking ensemble technique outperforms the others. This finding underscores the potential of advanced ensemble techniques in effectively addressing the dynamic and evolving cyber threats within SDN environments. Our study offers valuable insights into strengthening SDN infrastructures against DoS/DDoS attacks, contributing to the ongoing efforts to secure modern network architectures. © 2024 IEEE.

关键词： Denial-of-service attack

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Arrhythmia Classification Using Graph Neural Networks Based on Correlation Matrix

Arrhythmia Classification Using Graph Neural Networks Based ...

引用

2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Han, Seungwoo Tokyo University of Agriculture and Technology Dept. of Electrical Engineering and Computer Science Japan

ISBN: (纸本)9798350386226

With the advancements in graph neural network (GNN), there has been increasing interest in applying GNN to electrocardiogram (ECG) analysis. In this study, we generated an adjacency matrix using correlation matrix of extracted ECG features and applied a GNN to classify arrhythmias. Our work was compared with existing approaches from the literature. The results demonstrated that precision and recall for all arrhythmia classes exceeded 50%, suggesting that proposed method can be considered an approach for arrhythmia classification. © 2024 IEEE.

关键词： Graph neural networks

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Free Space Optical Communication Using Principles of Quantum Mechanics 4

Free Space Optical Communication Using Principles of Quantum...

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4th International Conference on Innovations in computer science, ICONICS 2024

作者： Angabeen, Tooba Asghar, Fatima Ali Zafar, Roohi Malik, Tahir Khan, Muhammad Mubashir Kamran, Muhammad NED University of Engineering and Technology Dept. of Physics Karachi Pakistan NED University of Engineering and Technology Dept. of Telecommunications Engineering Karachi Pakistan NED University of Engineering and Technology Dept. of Computer Science and Information Technology Karachi Pakistan

ISBN: (纸本)9798331504342

It all begun by the early 1970s through, the work of Stephen Wiesner and Gilles Brassard, who introduced the concept of quantum conjugate coding, and it has now stretched to a level where today's agile communication systems constitute a substantial challenge for data security and safety. A number of research has been performed to achieve secure communication among legitimate parties in which QKD (Quantum Key Distribution) is prominent. Quantum Key Distribution (QKD), also called Quantum Cryptography, enables two communicating parties to share secret cryptographic keys with security that is theoretically guaranteed by the laws of quantum mechanics, assuming ideal conditions. However, practical implementations may be vulnerable to certain attacks, and the security of QKD can be compromised by imperfections in hardware, side-channel attacks, or limitations in real-world setups. In a practical QKD setup, two stations send and receive encoded quantum particles and some classical information. The most rudimentary and well-known QKD protocol is BB84, developed by Bennet and Brassard in 1984. We have developed the simulation and achieved the secret shared key with and without an intruder. © 2024 IEEE.

关键词： Optical communication

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A Machine Learning Model for Predicting Internet Addiction in Teenage and Young Students 3

A Machine Learning Model for Predicting Internet Addiction i...

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3rd IEEE World Conference on Applied Intelligence and Computing, AIC 2024

作者： Johar, Geetika Patel, Ravindra University Institute of Technology Rgpv Dept. of Computer Science & Engineering Bhopal India University Institute of Technology Rgpv Dept. of Computer Applications Bhopal India

ISBN: (纸本)9798350384598

Internet addiction is becoming one of the critical issues among teenagers and young university students. This habit not only negatively impacts the student's learning performance, but also affects the student's physical and mental health. This paper is focusing on designing a Machine Learning (ML) model for accurately identifying internet addiction. The model incorporates data preprocessing and feature selection techniques for reducing the computational complexity. Next, five different ML algorithms have been implemented and compared to identify appropriate ML algorithms. The identified ML model has been trained using the behavioral features of the students for predicting internet addiction. The experiments have been carried out and the performance of the models has been measured. The results demonstrate that a random forest classifier provides 83% accurate detection for university students and 96% accurate results are provided by an SVM-based classifier for teenager internet addiction detection. Therefore, by using the entire observation of experiments it is found that the proposed model can successfully identify internet addiction in students by analyzing the student's behavioral features. © 2024 IEEE.

关键词： Contrastive Learning

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Efficiency Improvement Mechanism Analysis of Sidewall Passivation GaN based Micro-LEDs by Atomic Layer Deposition

Efficiency Improvement Mechanism Analysis of Sidewall Passiv...

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International Symposium, Seminar, and Exhibition, Display Week 2024

作者： Zhanghu, Mengyuan Liu, Yibo Liu, Zhaojun Dept. of Electric and Electronic Engineering Southern University of Science and Technology Shenzhen China Dept. of Electronic and Computer Engineering The Hong Kong University of Science and Technology Hong Kong

Sidewall passivation using atomic layer deposition (ALD) can efficiently improve external quantum efficiency (EQE). Such treatment increases EQE, a combination of internal quantum efficiency (IQE) and light extraction efficiency (LEE), primarily by curtailing surface recombination according to the ABC model. Determined by the ratio of radiative recombination rate to total recombination rate, the IQE of an LED holds a significant correlation with the diode's ideality factor in the Shockley model. In this paper, we analysis the mechanism of ALD sidewall passivation enhancing device performance in detail from the aspects of electrical performance improvement and optical performance improvement. © 2024, John Wiley and Sons Inc. All rights reserved.

关键词： Gallium nitride

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A Highly Scalable Automated Universal Binary Malware Analysis Framework for Healthcare and Medical SaaS 24

A Highly Scalable Automated Universal Binary Malware Analysi...

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8th International Conference on Medical and Health Informatics, ICMHI 2024

作者： Cheng, Shin-Ming Fan, Chin-Chi Chien, Bo-Han Chang Chen, Chia-Yi Lin, Wei-Hung Liao, Chen-Chien Dept. of Computer Science and Information Engineering National Taiwan University of Science and Technology Taipei City106335 Taiwan Dept. of Computer Science and Information Engineering National Taiwan University of Science and Technology Taipei City106335 Taiwan

ISBN: (纸本)9798400716874

In recent years, there has been a growing emphasis on information security, with major companies introducing cybersecurity teams to ensure the safety of data. However, there is a lack of convenient channels for general users to engage in cybersecurity protection, leaving them with no choice but to rely on various antivirus software installations to safeguard their privacy and financial assets. This phenomenon is particularly evident in the digital transformation of healthcare and medical information systems, where the substantial amount of digitized patient data has become a crucial asset within hospital systems. Unfortunately, it has also made healthcare systems potential targets for cyberattacks. The databases of major hospitals have become vulnerable to malicious virus invasions, posing significant threats to patient privacy and the operational integrity of healthcare institutions. Faced with such threats, there is an increasing need for comprehensive cybersecurity protection mechanisms. To address this issue, we have developed the Universal Binary Malware Analysis Framework (UBMAF), an easily accessible binary file analysis framework for the general public. UBMAF integrates multiple open-source static and dynamic analysis tools into an automated module, deployed as Software as a Service (SaaS) in the cloud for healthcare and medical systems. This eliminates the need for users to install applications, and the framework interface is optimized for intuitive usability. During the usage process, users can freely choose module combinations. After uploading files to UBMAF, the framework conducts corresponding tool analyses or file processing based on the selected modules. Ultimately, it provides users with downloadable results and analysis reports. This design enables large healthcare and medical systems to quickly and conveniently enhance their cybersecurity defenses while ensuring the security of digital medical data, effectively addressing the challenges brought abo

关键词： Cybersecurity

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Cardiovascular Disease Prediction Utilizing Machine Learning and Feature Selection with Clonal Selection Algorithm 5

Cardiovascular Disease Prediction Utilizing Machine Learning...

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5th International Conference on Sustainable Technologies for Industry 5.0, STI 2023

作者： Rahman, Wahidur Aneek, Riasaad Haque Moinuddin, Md. Sakib, Md. Shadman Iqbal, Md. Sadiq Rahman, Mohammad Motiur Uttara University Dept. of Computer Science and Engineering Uttara Dhaka Bangladesh Z.H. Sikder University of Science and Technology Dept. of Computer Science and Engineering Bhedarganj Shariatpur Bangladesh University of Information Technology and Sciences Dept. of Computer Science and Engineering Dhaka Bangladesh Bangladesh University Dept. of Computer Science and Engineering Adabor Bangladesh Mawlana Bhashani Science and Technology University Dept. of Computer Science and Engineering Tangail Bangladesh

ISBN: (纸本)9798350394290

A substantial and rising number of patients suffer from cardiovascular diseases, including heart attacks, heart failure, and other related illnesses. This case surge places increasing pressure on healthcare professionals and administrators while patients grapple with growing medical costs. To address these challenges, an automated system is necessary within the healthcare sector. In this paper, we present a cardiovascular health monitoring system that incorporates Machine Learning techniques. The study employed feature selection techniques on a secondary dataset. The study has used a nature-inspired technique, namely the Clonal Selection Algorithm (CSA) and Maximum Relevance Minimum Redundancy (mRMR) technique, to identify the most prominent feature in the detection of cardio-vascular illness. This approach was combined with a collection of Machine Learning classifiers. A group of experimental data has been listed to assess the suggested model's effectiveness. The research findings indicated a maximum accuracy rate of 100% when employing the proposed algorithms and orientations. The study also discusses the performance analysis for CSA and mRMR using a set of performance evaluation matrices. Based on the obtained results, it can be inferred that the suggested model will likely exhibit a high level of effectiveness in identifying cardiovascular diseases. © 2023 IEEE.

关键词： Feature Selection

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Heads-Up Multitasker: Simulating Atention Switching On Optical Head-Mounted Displays

Heads-Up Multitasker: Simulating Atention Switching On Optic...

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2024 CHI Conference on Human Factors in Computing Sytems, CHI 2024

作者： Bai, Yunpeng Ikkala, Aleksi Oulasvirta, Antti Zhao, Shengdong Wang, Lucia J. Yang, Pengzhi Xu, Peisen Dept. of Computer Science National University of Singapore Singapore Dept. of Computer Science Aalto University Espoo Finland Dept. of Information and Communications Engineering Aalto University Helsinki Finland School of Creative Media Dept. of Computer Science City University of Hong Kong Hong Kong Hong Kong Dept. of Electrical Engineering and Computer Science Massachusetts Institute of Technology Cambridge United States Dept. of Electrical Engineering Mathematics and Computer Science Delft University of Technology Delft Netherlands Dept. of Computer Science IPAL National University of Singapore Singapore

ISBN: (纸本)9798400703300

Optical Head-Mounted Displays (OHMDs) allow users to read digital content while walking. A better understanding of how users allocate attention between these two tasks is crucial for improving OHMD interfaces. This paper introduces a computational model for simulating users' attention switches between reading and walking. We model users' decision to deploy visual attention as a hierarchical reinforcement learning problem, wherein a supervisory controller optimizes attention allocation while considering both reading activity and walking safety. Our model simulates the control of eye movements and locomotion as an adaptation to the given task priority, design of digital content, and walking speed. The model replicates key multitasking behaviors during OHMD reading while walking, including attention switches, changes in reading and walking speeds, and reading resumptions. © 2024 Copyright held by the owner/author(s)

关键词： Reinforcement learning

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Classifying wireless IOT ICU traffic with machine learning models 19

Classifying wireless IOT ICU traffic with machine learning m...

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19th International Conference on Future Networks and Communications, FNC 2024 / 21st International Conference on Mobile Systems and Pervasive Computing, MobiSPC 2024 / 14th International Conference on Sustainable Energy Information technology, SEIT 2024

作者： Sibai, Fadi N. Sibai, Ahmad Electrical & Computer Engineering Dept. Gulf University for Science and Technology Mishref Kuwait Computer Science & Engineering Dept. University of South Florida TampaFL33620 United States

In this work, we classified the wireless internet of things (IoT) traffic of the IoT Health intensive care unit (IHI) dataset which belongs to three general classes: patient monitoring, environment monitoring, and network attack. We trained and tested 7 machine learning (ML) models with Orange 3 including kNN, Decision Tree (tree), SVM, Random Forest (RF), Neural Network (NN), Gradient Boosting (GB), and AdaBoost (AB). With the original dataset, 5 ML models performed perfect classification. After pruning the dataset columns by keeping the features with the highest correlations with the label in the dataset, good classifications were obtained with only 4 TCP/IP features by the Gradient Boosting, kNN, and RF models with MSEs in the range 0.008-0.011, and R2s in the range 0.978-0.984. With only 6 MQTT features, Gradient Boosting, RF, Tree, and NN were the top classifiers with MSEs in the range 0.073-0.074, and R2s in the range 0.856-0.859. This work demonstrates the effectiveness of guiding the feature pruning process by the values of the correlation coefficients in order to minimize the long training times of ML models while achieving good accuracies. © 2024 The Authors. Published by Elsevier B.V.

关键词： correlation coefficient feature pruning Internet of things IOT health intensive care unit dataset machine learning network security

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