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9th IEEE International Conference on Computing, engineering and Design, ICCED 2023

作者： Erfina, Adhitia Hidayat, Rahmat Rizaldi, Rafli Radya Puput Maulana, Riko Sembiring, Falentino Nusa Putra University Faculty of Engineering Computing and Design Departement of Infomation System Sukabumi Indonesia Budi Luhur University Master of Computer Science Faculty of Information Technology Jakarta Indonesia

ISBN: (纸本)9798350370126

The Android operating system released by Google in 2007 has experienced very significant development. In 2019, the number of Android application users reached 2.5 billion. One of the contributing factors is the nature of the android application, which is very flexible and easy to install. Users can download APK files on the internet. Generally, APK files are stored in archive format, so they are pretty easy to manage. However, APK files may be used to spread malware behind this convenience. This study aims to provide an overview of the process of planting malware/backdoor on Android applications and how to prevent it. This study initiated the injection of malicious code on five android applications, namely WhatsApp, Telegram, DroidCam, Fifa Mobile, and Tokopedia. According to the results, four applications were successfully injected, while one application failed. The failure most likely occurred as a result of the application's source code being obfuscated. Obfuscation is therefore strongly advised for Android applications to prevent code injection that could potentially lead to malware insertion. © 2023 IEEE.

关键词： Android (operating system)

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A Novel Architecture for Developing IoT Solutions Applied to Healthcare 2

A Novel Architecture for Developing IoT Solutions Applied to...

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2nd International Conference on Applied Artificial Intelligence and Computing, ICAAIC 2023

作者： Levina Kaliyamoorthi, P. Dattu, Vinjamuri S.N.Ch. Dineshnath, G. Jaiganesh, S. Dobhal, Dinesh Chandra Robin, C. R. Rene Kns Institute of Technology Department of Information Science & Engineering Karnataka Bangalore560064 India Madanapalle Institute of Technology & Science Department of Computer Science and Engineering Andhra Pradesh Madanapalle517325 India Aditya Engineering College Department of Mechanical Engineering Andhra Pradesh Surampalem533437 India Kl University Department of Computer Science and Engineering Andhra Pradesh Vaddeswaram522302 India Department of Master of Computer Applications Tamil Nadu Dindigul624622 India Graphic Era Deemed to Be University Department of Computer Science and Engineering Uttarakhand Dehradun248002 India Sri Sairam Engineering College Department of Computer Science and Engineering Tamil Nadu Chennai600044 India

ISBN: (纸本)9781665456302

The objective of this research study is to reduce the complexity and increase the flexibility to create and maintain computational solutions applied to health, where the scenarios are quite complex and dynamic. The main contributions of HealthDash are: (1) Provide dynamism in the process of creating IoT applications, both in the fog layer and in the cloud layer, through unification action of technologies adopted at both levels;(2) provide means to reduce the volume of information transmitted between the fog layer and the cloud layer, enabling the analysis and filtering of data closer to the edge of the network. This article proposes a solution for the development of IoT applications for health called HealthDash, based on the data flow-oriented programming paradigm, allowing to indicate in which layer of the architecture (fog or cloud) the data flows, which process data from the monitored patients, will be executed. © 2023 IEEE.

关键词： Internet of things

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AI-Driven Innovations in Modern Cloud Computing

arXiv

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arXiv 2024年

作者： Kumar, Animesh Master of Computer Science Illinois Institute of Technology IL United States Bachelor of Engineering in Computer Technology Nagpur University Maharashtra India

The world has witnessed rapid technological transformation, past couple of decades and with Advent of Cloud computing the landscape evolved exponentially leading to efficient and scalable application development. Now, the past couple of years the digital ecosystem has brought in numerous innovations with integration of Artificial Intelligence commonly known as AI. This paper explores how AI and cloud computing intersect to deliver transformative capabilities for modernizing applications by providing services and infrastructure. Harnessing the combined potential of both AI & Cloud technologies, technology providers can now exploit intelligent resource management, predictive analytics, automated deployment & scaling with enhanced security leading to offering innovative solutions to their customers. Furthermore, by leveraging such technologies of cloud & AI businesses can reap rich rewards in the form of reducing operational costs and improving service delivery. This paper further addresses challenges associated such as data privacy concerns and how it can be mitigated with robust AI governance *** Codes 68Txx - Artificial Intelligence Copyright © 2024, The Authors. All rights reserved.

关键词： Cloud platforms

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NextGen Robot: An Efficient and Effortless Wall Painting Robot Design Based on Artificial Intelligence (AI) Principles

NextGen Robot: An Efficient and Effortless Wall Painting Rob...

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2024 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems, ICSES 2024

作者： Mary, S. Jeya Sajithra, S. Divya, G. Lakshmi, N. Giri, Pallavi Al-Qawasmi, K. Einstein College of Engineering Department of Ece Tirunelveli India R.M.K. Engineering College Department of Computer Science and Engineering Chennai India Srm Institute of Science and Technology Faculty of Engineering and Technology Department of Networking and Communications TamilNadu Kattankulathur India S.A. Engineering College Department of Master of Computer Applications Chennai India Laxminarayan Innovation Technological University Department of Civil engineering Nagpur India Zarqa University Faculty of Information Technology Department of Cyber Security Zarqa Jordan University of Business and Technology Jeddah21448 Saudi Arabia

ISBN: (纸本)9798331543617

A fully autonomous wall-painting robot is the focus of this paper's development, expansion, and implementation efforts. Research efforts have not communicated much about interior wall painting. Painters' eyes and respiratory systems are particularly vulnerable to the toxic chemicals used in the industry. Additionally, the monotony, time commitment, and physical exertion inherent in the painting process are hard to resist. The entire construction process may be enhanced and time and labor saved by using robots and human workers appropriately into building jobs. The Dynamic Smart Painting Robo Model (DSPRM) is a novel robot that this study introduces for intense wall painting. To test its efficacy, it is crossvalidated with the usual technique, the usual Painting Bot (CPB). Furthermore, it would provide an opportunity to lessen or eliminate human exposure to challenging and dangerous conditions, which would address most of the issues, provide protection, and make it happen more often. An autonomous robotic painting system is being developed as a result of the wall painting robot. © 2024 IEEE.

关键词： Machine design

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A Productive Model for Secured Data Sharing in Blockchain technology based IoT 6

A Productive Model for Secured Data Sharing in Blockchain Te...

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6th International Conference on Inventive Computation Technologies, ICICT 2023

作者： Narayana Tinnaluri, V.S. Jyothi Babu, A. Shanmugaraja, P. Satheesh Kumar, S. AdityPai, H. Department of Computer Science Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram522502 India Department of Master of Computer Applications SreeVidyanikethan Engineering College Andhra Pradesh Tirupati517102 India Department of Information Technology Sona College of Technology Tamil Nadu Salem636005 India Department of Master of Business Administration Panimalar Engineering College Poonamallee Tamil Nadu Chennai600123 India Department of Computer Science and Engineering Graphic Era Deemed to be University Uttarakhand Dehradun248002 India

ISBN: (纸本)9798350398496

Authentication and access control are critical issues in IoT networks, as these networks comprise a large number of connected devices that exchange sensitive data. One of the biggest challenges in IoT authentication is the lack of uniformity among devices, which makes it difficult to implement a standard mechanism. Additionally, many IoT devices makes implementing robust authentication protocols challenging. Another challenge is the issue of access control, which is critical to ensure that only authorized users and devices can access sensitive data. Access control mechanisms should be implemented at various levels, including the device level, network level, and application level. However, in many cases, IoT devices lack the necessary features to implement advanced access control mechanisms. To address these challenges, IoT manufacturers and developers must prioritize security in their design and development processes. This includes implementing robust authentication and access control mechanisms, ensuring software and firmware updates are timely, and conducting regular security audits to identify and address vulnerabilities. Additionally, IoT devices should be designed with security in mind, and security features should be a core component of the device's design, rather than an afterthought. This work aims to solve the problem of authentication and access control in IoT networks, using Blockchain technology, proposing a consensus algorithm and a trust model between nodes. © 2023 IEEE.

关键词： Authentication

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A Comprehensive Study of a Conversational AI-Driven Healthcare Bot with Pose Estimation for Precision Fitness Monitoring and Personalized Guidance

A Comprehensive Study of a Conversational AI-Driven Healthca...

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2024 IEEE International Conference on Big Data and Machine Learning, ICBDML 2024

作者： Sinha, Anurag Kumar, Biresh Kundu, Roumo Sharma, Amit Sharma, Mukesh Raza, Asif ICFAI University ICFAI Tech School Computer Science Department Jharkhand Ranchi India Amity University Department of Computer Engineering and Application Jharkhand India Amity Institute of Information Technology Amity University Jharkhand Jharkhand Ranchi India Lovely Professional University Department of Computer Science Phagwara India Amity University Department of Master of Computer Application Jharkhand Ranchi India

ISBN: (纸本)9798350374100

This research investigates a new healthcare paradigm through the analysis of a conversational AI-powered healthcare bot that has posture estimation capabilities for accurate fitness tracking and customized recommendations. User motions during workout routines are accurately and thoroughly assessed thanks to the incorporation of powerful posture estimation technology. With the help of individualized exercise advice, this thorough study explores the capabilities of the system and shows how it may transform wellness practices. The healthcare bot provides an innovative method of fitness tracking by using conversational AI and accurate posture prediction to customize recommendations based on user requirements. This novel combination promises to enhance wellbeing and provide consumers with a more efficient and customized workout experience. The study emphasizes this point. © 2024 IEEE.

关键词： Health care

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A ShuffleNet Architecture-Based Multi-Layer Feature Scaling and Fusion Approach for Underwater Net Cage Damage Detection in Aquaculture

A ShuffleNet Architecture-Based Multi-Layer Feature Scaling ...

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IEEE International Conference on Consumer Electronics-Asia (ICCE-Asia)

作者： Xiang-Rui Huang Yi-Cheng Chen Liang-Bi Chen Wu-Chih Hu Department of Electrical Engineering Master Program in Electrical Engineering and Computer Science National Penghu University of Science and Technology Penghu Taiwan Department of Computer Science and Information Engineering National Penghu University of Science and Technology Penghu Taiwan

ISBN: (数字)9798331530839

ISBN: (纸本)9798331530846

In offshore aquaculture operations, personnel equipped with diving gear are often necessary to inspect the underwater net cages for damage, particularly on the sea floor. This manual inspection process is time-consuming and complex. To overcome this problem, this paper proposes a computer vision solution for identifying damage in underwater net cages to address the inefficiencies and challenges of traditional manual inspections. The proposed scheme utilizes a high-performance multi-branch computational architecture designed based on ShuffleNet architecture to detect net cage damage more efficiently. Experimental results demonstrate that this work performs well on the ImageNet ILSVRC-2010 dataset and achieves an accuracy of 88.54% in underwater net damage detection.

关键词： Accuracy Gears Computational modeling Sea floor computer architecture Manuals Inspection Feature extraction Personnel Aquaculture

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Developing Instruments for Digital Talent Competence Using Partial Least Square-Based Models 11

Developing Instruments for Digital Talent Competence Using P...

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11th International Conference on Cyber and IT Service Management, CITSM 2023

作者： Wahyudi, Mochamad Chakim, Mochamad Heru Riza Henderi Sudaryono Si, Muhamad Viktor A Saputra, Dimas Bagus Rahardja, Reymund Master of Computer Science University of Bina Sarana Informatika Tangerang Indonesia University of Raharja Master of Retail Management Tangerang Indonesia University of Raharja Master of Informatics Engineering Tangerang Indonesia University of Raharja Master of Digital Business Tangerang Indonesia University of Raharja Department of Information Technology Tangerang Indonesia University of Raharja Department of Digital Business Tangerang Indonesia

ISBN: (纸本)9798350305968

Research on measuring digital talent competencies using explainable artificial intelligence is lacking in depth. The study aimed to address this gap by developing a reliable and valid measurement instrument for digital talent. SmartPLS 4.0 was employed for calculations and quality assessment. The measurement model was assessed from both reflective and formative perspectives, using Google Forms to gather responses from 600 participants. The 60 questionnaire items exhibited high validity and reliability, as evidenced by loading factors and composite reliability exceeding 0.70. Direct effects were found in various competencies: adaptability and flexibility had a 0.744 effect on learning dexterity, achievement orientation showed a 0.782 effect on innovative creation skills, customer service orientation had a 0.810 effect on digital networking, and digital communication skills exhibited a 0.824 effect on continual improvement. The measurement model met various criteria for reliability and validity, including loading factors, composite reliability, average variance extracted, cross loadings, Fornell Lacker Criterion, and Heterotrait Monotrait Ratio. These findings supported the acceptance of the measurement model's quality and validity. SmartPLS analysis yielded an average communality of 0.624 and an average R Square of 0.592, resulting in a GoF index of 0.608. This GoF index, according to Wetzel et al. (2018), falls in the high fit category, indicating the model's appropriateness for measuring digital talent competencies. In summary, the study successfully developed a robust measurement model for digital talent competencies using explainable artificial intelligence and demonstrated its high fit and reliability for assessing digital talent in various contexts. © 2023 IEEE.

关键词： Reliability

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Clustering Analysis of a Spatiotemporal Dataset with a Novel Kernel Density Estimator

Clustering Analysis of a Spatiotemporal Dataset with a Novel...

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2023 International Conference on Machine Learning and Cybernetics, ICMLC 2023

作者： Yu, Jen-Chien Yang, Chun-Chieh Gilbert, John Reuben Liu, Rou-Jun Oyang, Yen-Jen Yang, Meng-Han National Kaohsiung University of Science and Technology Department of Computer Science and Information Engineering Kaohsiung City807618 Taiwan National Taiwan University Master Program in Statistics Taipei106216 Taiwan National Taiwan University Department of Computer Science & Information Engineering Taiwan Graduate Institute of Biomedical Electronics and Bioinformatics National Taiwan University Taiwan

ISBN: (纸本)9798350303780

A vast number of spatiotemporal datasets collected from a wide range of sources has motivated scientists to develop effective approaches to identify interesting patterns hidden in these datasets. In this respect, kernel density estimators, which belong to a class of non-parametric estimators in statistics, have been widely exploited in recent years. With this background, we have developed a novel kernel density estimator aiming to provide accurate analysis results. According to the evaluation with a real spatiotemporal dataset, which collected emergency medical service records in a county in the United States, the proposed kernel density estimator can approximate the probability density function significantly more accurately than a conventional kernel density estimator. Furthermore, we have exploited the proposed kernel density estimator to identify interesting patterns hidden in the real spatiotemporal dataset. © 2023 IEEE.

关键词： Probability density function

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RUL Estimation for Power Electronic Devices Using RNNs

RUL Estimation for Power Electronic Devices Using RNNs

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Prognostics and System Health Management Conference (PHM)

作者： Congrui Cai Zhonghai Lu Embedded Systems Master Program School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Department of Electrical Engineering School of Electrical Engineering and Computer Science KTH Royal Institute of Technology

ISBN: (数字)9798350360585

ISBN: (纸本)9798350360592

Recurrent Neural Networks (RNNs) are commonly used in data-driven approaches to estimate the Remaining Useful Lifetime (RUL) of power electronic devices. RNNs are preferred because their intrinsic feedback mechanisms are better suited to model time-series data. However, the impact of RNN complexity on estimation accuracy is rarely discussed in the literature. This issue is important because choosing a lower-complexity model that delivers the same or similar performance as a higher-complexity model can increase implementation efficiency. In the paper, we use three RNN models, namely, the vanilla version, LSTM (Long Short Term Memory) and GRU (Gated Recurrent Unit) to conduct RUL estimation for power electronic devices. We use two accelerated aging datasets, one dataset targeting the package failure of MOSFETs, and the other dataset targeting package failure of power diodes. Our study shows that a lower-complexity RNN does not necessarily deliver a lower performance. Similarly, a higher-complexity model does not assure a higher performance. As such, our work highlights the importance of selecting a proper neural network for RUL estimation not biased towards complex models. This is especially useful and important for implementing such RUL estimation techniques in embedded resource-constrained and speed-limited computins platforms.

关键词： Performance evaluation Semiconductor device modeling Recurrent neural networks Computational modeling Estimation Power electronics Data models

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