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Design, Automation and Test in Europe Conference and Exhibition

作者： Cristian Turetta Geri Skenderi Luigi Capogrosso Florenc Demrozi Philipp H. Kindt Alejandro Masrur Franco Fummi Marco Cristani Graziano Pravadelli Department of Computer Science University of Verona Italy Department of Electrical Engineering and Computer Science University of Stavanger Norway Faculty of Computer Science TU Chemnitz Germany

In the context of smart buildings and smart cities, the design of low-cost and privacy-aware solutions for recognizing the presence of humans and their activities is becoming of great interest. Existing solutions exploiting wearables and video-based systems have several drawbacks, such as high cost, low usability, poor portability, and privacy-related issues. Consequently, more ubiquitous and accessible solutions, such as WiFi sensing, became the focus of attention. However, at the current state-of-the-art, WiFi sensing is subject to low accuracy and poor generalization, primarily affected by environmental factors, such as humidity and temperature variations, and furniture position changes. Such is-sues are partially solved at the cost of complex data preprocessing pipelines. In this paper, we present a highly accurate, resource-efficient deep learning-based occupancy detection solution, which is resilient to variations in humidity and temperature. The approach is tested on an extensive benchmark, where people are free to move and the furniture layout does change. In addition, based on a consolidated algorithm of explainable AI, we quantify the importance of the WiFi signal w.r.t. humidity and temperature for the proposed approach. Notably, humidity and temperature can indeed be predicted based on WiFi signals; this promotes the expressivity of the WiFi signal and at the same time the need for a non-linear model to properly deal with it.

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A Review on Blockchain Technologies for an Advanced and Cyber-Resilient Automotive Industry

arXiv

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arXiv 2024年

作者： Fraga-Lamas, Paula Fernández-Caramés, Tiago Manuel Department of Computer Engineering Faculty of Computer Science Universidade da Coruña Campus de Elviña s/n A Coruña15071 Spain

In the last century the automotive industry has arguably transformed society, being one of the most complex, sophisticated and technologically advanced industries, with innovations ranging from hybrid, electric and self-driving smart cars to the development of IoT-connected cars. Due to its complexity, it requires the involvement of many Industry 4.0 technologies, like robotics, advanced manufacturing systems, cyber-physical systems or augmented reality. One of the latest technologies that can benefit the automotive industry is blockchain, which can enhance its data security, privacy, anonymity, traceability, accountability, integrity, robustness, transparency, trustworthiness and authentication, as well as provide long-term sustainability and a higher operational efficiency to the whole industry. This review analyzes the great potential of applying blockchain technologies to the automotive industry emphasizing its cybersecurity features. Thus, the applicability of blockchain is evaluated after examining the state-of-the-art and devising the main stakeholders’ current challenges. Furthermore, the article describes the most relevant use cases, since the broad adoption of blockchain unlocks a wide area of short- and medium-term promising automotive applications that can create new business models and even disrupt the car-sharing economy as we know it. Finally, after a Strengths, Weaknesses, Opportunities, and Threats (SWOT) analysis, some recommendations are enumerated with the aim of guiding researchers and companies in future cyber-resilient automotive industry developments. © 2024, CC BY.

关键词： Cybersecurity

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Enhancing Rehabilitation Outcomes: A Hybrid Cognitive-Machine Learning Approach

Enhancing Rehabilitation Outcomes: A Hybrid Cognitive-Machin...

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International Conference on Communication and Electronics Systems (ICCES)

作者： Vijaya Chandra Jadala S. Hrushikesava Raju S. Adinarayna Nabanita Choudhury Shaik Jumlesha U. Sesadri School of Computer Science and Artificial Intelligence SR University Warangal Telangana India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Green Fields Vaddeswaram Guntur Department of Computer Science and Systems Engineering College of Engineering Andhra University Visakhapatnam Faculty of Computer Technology Assam down town University Guwahati Assam Department of CSE Annamacharya Institute of Technology and Science AP Department of CSE Vardhaman College of Engineering Hyderabad Telangana

ISBN: (数字)9798350377972

ISBN: (纸本)9798350377989

Traditional rehabilitation methods often focus on a single impairment, leading to challenges such as low motivation and inadequate responses to the diverse needs of patients, time taken for appointments, and lack of personal monitoring of health status. To address these issues, The model employs meta-cognitive strategies, dynamic adaptive training, and modified Convolutional Neural Networks (CNN) within an ensemble framework, targeting multiple impairments while tailoring interventions to individual patient requirements. A hybrid model proposed would integrate cognitive and machine learning methodologies to enhance personalized rehabilitation. By selectively employing meta-cognitive strategies, dynamic adaptive training, and modified Convolutional Neural Networks (CNN) within an ensemble framework, the proposed system aims to bridge existing gaps in rehabilitation practices. The hybrid system is designed to efficiently manage patient data, reduce the complexity of rehabilitation tasks, and optimize computational resources. The use of stacking as an ensemble method further mitigates biases and uncertainties associated with individual models, thereby improving the reliability of the rehabilitation framework. Preliminary outcomes indicate that this hybrid approach achieves superior accuracy and efficiency compared to traditional methods, ultimately enhancing adaptability to the varied needs and preferences of patients.

关键词： Training Adaptation models Accuracy Adaptive systems Uncertainty Computational modeling Stacking Convolutional neural networks Reliability Optimization

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Feed force based optimization of process parameters by bio-nanofluid for machining SAE 1045 steel

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Materials Today: Proceedings 2022年 62卷 1319-1324页

作者： Gnanavel, C. Saravanan, R. Malladi, Avinash Sekar, C.B. Rajesh, S. Nagarathi, D.K. Vels University Tamil Nadu Chennai 600117 India Saveetha University Tamil Nadu Chennai 602105 India Department of Mechatronics Engineering Faculty of Science and Technology ICFAI Tech. ICFAI Foundation for Higher Education Telangana State Hyderabad India Department of Mechanical Engineering Tagore Engineering College Tamil Nadu Chennai 600126 India Department of Mechanical Engineering Meenakshi College of Engineering Tamil Nadu Chennai 600 078 India Department of Computer Science Annamalai University Chidambaram. & Office of Registrar Tamil Nadu Teachers Education University Tamil Nadu Chennai 600097 India

Waste generated from shop of Kerala Chips is utilized for preparing a novel Nano fluid. Such Nano fluid can be alternatively used for lubricating purpose for machining the precision shaft. This piece of research work investigating the effect of Nano fluid of Nano Nano-Boracic acid particles concentrated used coconut oil (Waste) in machining shat. The objective is analyzing the machinability performance and optimize process parameters based on the response of Feed force. Used coconut oil (Waste)/ Nano-Boracic acid particles, bio- Nanofluid synthesized and tested in semi-automated heavy-duty lathe. The cutting speed varied between 40 m/min to 190 m/min, the tool feed rate given in the rage of 0.05 mm/rev to 0.20 mm/rev as finish cut, the tool tip's nose radius varied from 0.30 mm to 1.20 mm. all three factors considered to feed at 4 levels so Taguchi L16 orthogonal array type experimental design utilized. Taguchi analysis, ANOVA and T -test used for analyzing results, study the factors influences and optimizing the process parameters. The finding was the proposed bio-nanofluid performance is found good and significant. The process parameters optimized and presented the factors and levels contribution in minimizing the feed force in the finishing the steel shaft. © 2022

关键词： Nanofluidics

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Critical Success Factors of Operational Excellence in Software Quality Assurance: Best Practices for Integrated Change Control Management 19

Critical Success Factors of Operational Excellence in Softwa...

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19th IEEE International Colloquium on Signal Processing and Its Applications, CSPA 2023

作者： Wong, Whee Yen Sam, Toong Hai Too, Chian Wen Khin, Aye Aye Universiti Tunku Abdul Rahman Kajang Department of Internet Engineering and Computer Science Malaysia INTI International University Faculty of Business AndCommunications Seremban Nilai Malaysia Universiti Tunku Abdul Rahman Department of Economics Kajang Malaysia

ISBN: (纸本)9781665476928

Integrated Change Control Management(ICCM) is a crucial phenomenon in the IT and Software Development industry. Through ICCM, project managers can avoid all unnecessary changes that disrupt the project execution and implementation;which further contribute to project delay, cost overrun, endless rework and facing the prolonged acceptance of User Acceptance Test (UAT). However, it is common that many project managers place much emphasis of time and effort in producing a detailed and comprehensive software quality assurance plan (SQAP) as part of ICCM adoption and implementation;neglecting and overlooking other important factors which indeed contribute to successful implementation of operation excellence. As a result, understanding the critical success factors (CRFs) allows the project managers to drive best practices to work routines;indirectly encouraging and enforcing project team members to understand and commit to the assigned task effectively. This paper aims to outline the critical success factors of operational excellence of ICCM in the context of the IT industry to exceed customer expectations through best practices in improving project mindset, processes and culture. In nutshell, every team member will understand the ''flow of values'' to the customers;fostering a competitive advantage within the industry in the long term. © 2023 IEEE.

关键词： Acceptance tests

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Impact on mental health due to COVID-19 pandemic: A cross-sectional study in Bangladesh

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Clinical eHealth 2023年第1期6卷 42-52页

作者： Pathan, Refat Khan Biswas, Munmun Yasmin, Suraiya Uddin, Mohammad Amaz Das, Anindya Khandaker, Mayeen Uddin Faruque, M.R.I. Sarker, Moniruzzaman Dept. of Computing and Information Systems School of Engineering and Technology Sunway University Selangor Bandar Sunway 47500 Malaysia Dept. of Computer Science and Engineering BGC Trust University Bangladesh Chittagong 4381 Bangladesh Department of Computer and Information Science Tokyo University of Agriculture and Technology Koganei - 184-0012 Tokyo Japan Dept. of Computer Science and Engineering Chittagong University of Engineering and Technology Chittagong 4349 Bangladesh Dept. of Electrical and Electronic Engineering Chittagong University of Engineering and Technology Chittagong 4349 Bangladesh Center for Applied Physics and Radiation Technologies School of Engineering and Technology Sunway University Selangor Bandar Sunway 47500 Malaysia Space Science Centre Universiti Kebangsaan Malaysia 43600 UKM Selangor Bangi Malaysia Department of Marketing Sunway University Business School Sunway University Selangor Bandar Sunway 47500 Malaysia Faculty of Graduate Studies Daffodil International University Daffodil Smart City Birulia Savar Dhaka – 1216 Bangladesh

The government of Bangladesh has implemented the “Stay Home” policy following the WHO recommendation to resist the community transmission of Covid-19. As a result, the routine activities of all government, semi-government establishments, including educational institutions, are severely affected, and the country's economic growth becomes slowed down. To overcome such a situation, the relevant authorities have introduced the “Work from Home” policy for the employees and “Remote Education” for students. However, due to the persistence of multi-dimensional socio-economic problems, many employees and students face big challenges in performing their regular jobs while adopting such a policy. Consequently, enormous psychological anxiety has been developed for all people, including students, parents, employees, etc., and concurrently created severe changes in their behavior. This study aims to analyze the reasons for the behavioral changes of the employees, students, academic staff, and family members of different ages due to psychological anxiety, stress, or physical issues. A comprehensive online-based survey has been carried out on people working in various sectors in Bangladesh. A modified Apriori Algorithm has been used to sort out the associations between the causes and types of behavioral changes. Analyzed data revealed a massive human behavioral change in most participants. This finding indicates that the negligence of those significant human behavioral changes may cause a higher risk of creating psychological imbalance. Therefore, there is a need to have a solid understanding of the reasons for the behavioral changes and set up standard guidelines to maintain “Work from Home” in this Covid-19 situation to avoid psychological imbalance. Based on this study, some suggestions have been given for implementation by the government on an urgent basis. © 2023

关键词： Apriori algorithm Behavioral changes Covid-19 outbreak Family crisis Work from home

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Pharmacy Management System using Artificial Intelligence

Pharmacy Management System using Artificial Intelligence

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Machine Learning and Autonomous Systems (ICMLAS), International Conference on

作者： Om Nishane Vinod Ajankar Department of Computer Science & Engineering Faculty of Engineering & Technology Datta Meghe Institute of Higher Education and Research Wardha Maharastra India Faculty of Engineering & Technology Datta Meghe Institute of Higher Education and Research Wardha Maharastra India

ISBN: (数字)9798331505745

ISBN: (纸本)9798331505752

Pharmacy Management System (PMS) is an advanced software solution designed to increase the efficiency, accuracy and safety of pharmaceutical operations. System automates important functions such as inventory management, prescription processing, billing and compliance tracking, thus enables spontaneous pharmacy operations and enhances patient care. With integration of upcoming technologies such as Artificial Intelligence (AI), Cloud Computing, and Barcode scanning, PMS reduces errors, eliminates stockouts, and increases drug safety. The system also includes real-time analytics and reporting, which enables decision making and adaptation to resource use. The upcoming technologies such as IoT-based intelligent inventory management and integration give the PMS in the event of changing the pharmacy management by ensuring enhancement of accessibility, streamlining operations and compliance. The project emphasizes the importance of digital change in modern healthcare, which has increased service distribution and improves the patient's satisfaction.

关键词： Performance evaluation Drugs Accuracy Reviews Medical services Inventory management Software Safety Security Robots

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Optimizations of reinforcing particulates and processing parameters for stir casting of aluminium metal matrix composites for sustainable properties

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Materials Today: Proceedings 2022年 68卷 1172-1179页

作者： Kumar, Jatinder Kumar, Vipan Sharma, Shubham Chohan, Jasgurpreet Kumar, Raman Singh, Sandeep Obaid, Ahmed J. Akram, Shaik Vaseem Department of Mechanical Engineering St. Soldier Institute of Engineering and Technology Punjab Jalandhar144011 India Department of Mechanical Engineering IK Gujral Punjab Technical University Jalandhar-Kapurthala Road Punjab Kapurthala144603 India Chandigarh University Gharuan140413 India Department of Civil Engineering Chandigarh University Gharuan140413 India Faculty of Computer Science and Mathematics University of Kufa Iraq Division of Research and Innovation Uttaranchal University Uttarakhand Dehradun248007 India

Due to its exceptional characteristics, aluminium matrix composites are used in defence, automotive, and structural applications. Stir casting for SiC and Gr reinforced AMCs was optimised. SiC weight was kept constant during manufacturing, while Gr was 1, 3, and 5% by weight. Stir casting was used to formulate AMCs by varying graphite amounts, stirring time, and stirring speed according to Taguchi's L9 OA. Hardness, tensile strength, and porosity levels of composites were examined. Taguchi analysis and Grey Relational Analysis optimised stir casting. Stirring speed, duration, and graphite content were found to affect AMC hardness. Graphite particles influence AMC strength more than stirring speed and time. GRA results show that include Gr in AMCs is the most important component impacting hybrid AMC characteristics, contributing 63.83 percent to optimal response values. Stirring speed and time contribute 17.27% and 11.1% to optimal response values. Comparing anticipated and experimental GRG validates results. © 2022

关键词： Silicon carbide

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Frontloading in powertrain development involving hybrid (virtual and real) development environments

Transportation Engineering

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Transportation engineering 2023年 12卷

作者： Priebe-Keller, Lukas Netzel, Thomas Ihme-Schramm, Hanno Department Automotive and Aeronautical Engineering Faculty of Engineering and Computer Science Hamburg University of Applied Sciences Berliner Tor 9 Hamburg20099 Germany

In order to reduce the powertrain development time in the context of increasing system complexity, hardware-related development steps are being shifted to early phases using virtual development environments (frontloading) and agile working methods are being introduced. However, implementation in industrial practice is often insufficient. The application of virtual development environments is often not well accepted, especially since accurate, validated simulation models are not available until late in the development process and the competences/expertise of experts in the development environments of different degrees of virtualisation are often insufficiently brought in jointly and in an agile approach during their development. This is where the publication steps in, introducing a development approach for the agile development, application and further development of validated simulation models and presenting its application and added value on the basis of two practice-relevant case studies (powertrain design, development of torque coordination). It is shown that how real and virtual development environments can be applied in an interconnected way so that frontloading in the real development process succeeds in an agile way. The resulting outcomes are discussed and summarised. © 2023

关键词： Powertrains

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Improving bot detection of fake followers on Twitter via a hybrid B-HC optimisation algorithm

Improving bot detection of fake followers on Twitter via a h...

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International Conference on Electronics, Communication and Computing Technologies (ICECCT)

作者： Haider Alzeyadi Fecir Duran Department of Computer Science Informatics Institute Gazi University Ankara Turkey Department of Computer Engineering Engineering Faculty of Technology Gazi University Ankara Turkey

Sophisticated cyber threats are seen on Online Social Networks (OSNs) social media accounts automated to imitate human behaviours has an impactful effect on distorting public thoughts and opinions. OSNs are weaponized to diffuse deception, misinformation, and malicious activities, that forms a serious threat to society. The deceptive nature of imitating human behaviour has become a challenging and crucial task to detect automated accounts (socialbots). This research, however, proposes a hybrid metaheuristic optimisation algorithm for socialbot detection. Specifically, a hybrid B-Hill Climbing (B-HC) optimisation algorithm works in tandem with a k-NN nearest neighbour classifier to accurately select a relevant feature subset. It is applied to be tested for fake followers account on Twitter data. Experimental results showed that the proposed method is better than the traditional and the latest feature selection techniques as well as the rule-set methods. The B-HC alongside with k-NN method achieved promising results using only relevant feature subset.

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