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3D printing: Making an innovative technology widely accessible through makerspaces and outsourced services

Materials Today: Proceedings 2022年 49卷 2712-2723页

作者： Antreas Kantaros Olaf Diegel Dimitrios Piromalis Georgios Tsaramirsis Alaa Omar Khadidos Adil Omar Khadidos Fazal Qudus Khan Sadeeq Jan Department of Industrial Design and Production Engineering University of West Attica Greece Faculty of Engineering University of Auckland Auckland 1010 New Zealand Higher Colleges of Technology Women's College Abu Dhabi United Arab Emirates Department of Information Systems Faculty of Computing and Information Technology King Abdulaziz University Jeddah 21577 Saudi Arabia Department of Information Technology Faculty of Computing and Information Technology King Abdulaziz University Jeddah 21577 Saudi Arabia Department of Information Technology University of Swat Pakistan Department of Computer Science & IT University of Engineering & Technology Peshawar Pakistan

This paper looks into the emerging trends of makerspaces and outsourced 3D printing services, and examines the natural progression between them. Also, by presenting survey empirical data, it investigates the reasons why individual users and enterprises have started to choose such services and the benefits acquired from this choice, that lead to the promotion of creativity, innovation, and competition. Fundamental working principles both for makerspaces and outsourced 3D printing services are being analyzed. Through the conduction of a survey among two different makerspaces’ users, authors attempt to further clarify the factors that motivate users to choose to participate in makerspaces or use the services offered by an outsourced 3D printing provider. 3D printing technologies have been recognized as being the new industrial revolution. However, because of the nature of the additive manufacturing process, a high level of expertise is required in order to accomplish an acceptable result. The recent proliferation of makerspaces as well as outsourced 3D printing services offers alternative solutions towards overcoming the aforementioned challenges. Makerspaces are currently growing rapidly, aiming to help local communities to get accustomed with emerging manufacturing technologies such as 3D printing, especially in modern STEM educational practices. On the other hand, outsourcing has emerged as one of the top business practices of our time due to its numerous benefits for the companies involved. The paper makes a comparison between makerspaces and outsourced services and presents relevant original survey data from users.

关键词： 3D printing Makerspace Fab lab 3D printing services Outsourcing Survey

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Revolutionizing Medical Diagnostics: Exploring Creativity in AI for Biomedical Image Analysis

Revolutionizing Medical Diagnostics: Exploring Creativity in...

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Signal Processing, Computation, Electronics, Power and Telecommunication (IConSCEPT), International Conference on

作者： P. Krishnamoorthy Devulapally Swetha Pamidimukkala Sai Geetha K. Karunambiga Ramesh Kumar Ayyasamy Ajmeera Kiran PG Department of Computer Science Thiruthangal Nadar College Chennai Tamil Nadu India Department of Computer Science and Engineering Vaagdevi Engineering College Bolli Kunta Telangana India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram Andhra Pradesh India Department of CSE Karpagam Institute of Technology Coimbatore Tamil Nadu India Faculty of Information and Communication Technology Universiti Tunku Abdul Rahman (UTAR) Kampar Malaysia Department of Computer Science and Engineering MLR Institute of Technology Hyderabad India

ISBN: (数字)9798331540685

ISBN: (纸本)9798331540692

Biomedical image analysis plays a crucial role in the early and accurate diagnosis of diseases, significantly impacting patient outcomes. This study presents an innovative approach to biomedical image analysis by integrating machine learning techniques with a user-friendly web interface. The system takes input images uploaded through the web interface and processes them using advanced machine learning algorithms implemented in Python. The goal is to automate the detection of diseases from these images, enhancing the efficiency and precision of diagnosis. Convolutional Neural Networks (CNNs), a specialized class of deep learning models designed for image analysis, are employed to learn and characterize disease-specific patterns. The proposed approach is evaluated using diverse biomedical images encompassing various diseases and conditions. Extensive experiments demonstrate the system's effectiveness, achieving high accuracy, sensitivity, and specificity in disease detection. Integrating machine learning with a web interface simplifies the diagnostic process and enables remote consultations, making healthcare services more accessible and timelier. In conclusion, this study presents a robust framework for automated biomedical image analysis, demonstrating the potential of machine learning and web technologies in revolutionizing healthcare diagnostics. The system's accuracy and user-friendly interface make it a valuable tool for healthcare professionals, contributing to early disease detection and improved patient care.

关键词： Accuracy Machine learning algorithms Image analysis Medical services Signal processing Sensitivity and specificity Robots

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Camera deflection detection and image correction for applications in ADAS algorithms

Camera deflection detection and image correction for applica...

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2021 Zooming Innovation in Consumer Technologies Conference, ZINC 2021

作者： Sabo, Zeljko Grbic, Ratko Marinkovic, Vladimir Dukic, Miodrag RT-RK Institute for Computer Based Systems Osijek Croatia Computer Science and Information Technology Osijek Faculty of Electrical Engineering Osijek Croatia University of Novi Sad Faculty of Technical Sciences Novi Sad Serbia

ISBN: (纸本)9781665404174

Cameras that are mounted on almost every modern vehicle can be deflected due to the different reasons such as mechanical or environmental. This deviation of the camera from reference position can affect performance of the camera based Advanced Driving Assistant Systems (ADAS) such as lane detection. In this paper one possible approach for camera deflection detection and image correction is given. The proposed approach is based on estimation of homography matrix that is used for image transformation after which nearest-neighbor interpolation is performed. The presented algorithm for image correction is implemented in TDA2xx System-On-Chip on ADAS Alpha development board. The approach is evaluated for different deflections of the automotive camera from the reference position. © 2021 IEEE.

关键词： Cameras

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Optimizing Large-Scale Data Processing with Artificial Intelligence

Optimizing Large-Scale Data Processing with Artificial Intel...

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Smart Generation Computing, Communication and Networking (SMART GENCON), International Conference on

作者： Saniya Khurana Ritesh Kumar Beemkumar N Anupam Shukla D. Nirmala Vidula V. Meshram Centre of Interdisciplinary Research in Business and Technology Chitkara University Institute of Engineering and technology Chitkara University Punjab India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Department of Mechanical Engineering Faculty of Engineering and Technology JAIN (Deemed-to-be University) Bangalore Karnataka India Department of Computer Science & Engineering Vivekananda Global University Jaipur India Department of Electronics and Communication Engineering Prince Shri Venkateshwara Padmavathy Engineering College Chennai Department of Computer Engineering Vishwakarma Institute of Information Technology Pune India

massive-scale statistics processing presents precise demanding situations in extracting insights, as the sheer amount of records can be overwhelming for classic analytics systems. Artificial Intelligence (AI) can offer a powerful answer for coping with large-scale records units, because it leverages system mastering algorithms to construct sophisticated fashions and automate complex evaluation. AI algorithms are also adaptive and incredibly green, which reduces the time it takes to mine valuable insights from big information units. This paper will study the diverse approaches that AI can enhance large-scale information processing and explore the numerous advantages of deploying AI in statistics processing workflows. The dialogue will include the advantages of leveraging AI for both supervised and unsupervised getting to know tasks in addition to the technical concerns around deciding on the proper AI technology for information processing jobs. Subsequently, the paper will present actual- world applications of AI in huge-scale records processing and evaluate the ability for groups to gain an aggressive aspect by deploying AI of their information pipelines.

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Malware Attacks Detection in IoT Using Recurrent Neural Network(RNN)

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Intelligent Automation & Soft Computing 2024年第2期39卷 135-155页

作者： Abeer Abdullah Alsadhan Abdullah A.Al-Atawi Hanen karamti Abid Jameel Islam Zada Tan N.Nguyen Computer Science Department Imam Abdulrahman Bin Faisal UniversityDamam32232Saudi Arabia Department of Computer Science Applied CollegeUniversity of TabukTabuk47512Saudi Arabia Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Sciences and Information Technology Hazara UniversityMansehraPakistan Faculty of Computing Department of Software EngineeringInternational Islamic UniversityIslamabadPakistan Department of Architectural Engineering Sejong UniversitySeoul05006Korea

IoT(Internet of Things)devices are being used more and more in a variety of businesses and for a variety of tasks,such as environmental data collection in both civilian and military *** are a desirable attack target for malware intended to infect specific IoT devices due to their growing use in a variety of applications and their increasing computational and processing *** this study,we investigate the possibility of detecting IoT malware using recurrent neural networks(RNNs).RNNis used in the proposed method to investigate the execution operation codes of ARM-based Internet of Things apps(OpCodes).To train our algorithms,we employ a dataset of IoT applications that includes 281 malicious and 270 benign pieces of *** trained model is then put to the test using 100 brand-new IoT malware samples across three separate LSTM *** exposure was not previously conducted on these *** newly crafted malware samples with 2-layer neurons had the highest accuracy(98.18%)in the 10-fold cross validation experiment.A comparison of the LSTMtechnique to other machine learning classifiers shows that it yields the best results.

关键词： Malware malicious code code obfuscation IoT machine learning deep learning

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Virtual Horizons: Pioneering the Metaverse in Education and Industry Across ASEAN and Beyond

Virtual Horizons: Pioneering the Metaverse in Education and ...

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International Conference on Electrical engineering/Electronics, computer, Telecommunications and information technology, ECTI-CON

作者： Ittipon Yamyuan Sushank Chaudhary Muhammad Saadi Piya Kovintavewat Juthatip Wisanmongkol Mitsunori Makino La-or Kovavisaruch Kamol Kaemarungsi Taweesak Sanpechuda Lunchakorn Wuttisittikulkij Nitthita Chirdchoo Petchpilin Tontiwinyoopong Pisit Vanichchanunt Pruk Sasithong Suebphong Noisri Avishi Waidyaratne Panithan La-aiddee Rosa Delima Department of Electrical Engineering Chulalongkorn University Thailand Department of Electrical Engineering University of Central Punjab Pakistan Faculty of Science and Technology Nakhon Pathom Rajabhat University Nakhon Pathom Thailand National Electronics and Computer Technology Center Pathumthani Thailand Department of Information and System Engineering Chuo University Japan Department of Electrical and Computer Engineering Faculty of Engineering King Mongkut's University of Technology North Bangkok Bangkok Thailand Informatics Department Universitas Kristen Duta Wacana Yogyakarta Indonesia

ISBN: (数字)9798350381559

ISBN: (纸本)9798350381566

The integration of the Metaverse into the educational sector heralds a transformative era in teaching and learning paradigms, with the ASEAN region at its forefront. This manuscript examines the Metaverse's potential to transcend traditional educational barriers such as resource limitations, geographic isolation, and the lack of personalized learning. Active case studies from Nakhon Pathom Rajabhat University (NPRU), Thailand, the University of Central Punjab (UCP), Pakistan, the National Electronics and computer technology Center, Thailand, and Chuo University, Japan, are presented to offer a comprehensive overview of the Metaverse's current applications. These case studies showcase the Metaverse's role in creating inclusive, immersive, and engaging educational experiences. The paper identifies critical research gaps in technological feasibility, pedagogical adaptation, and equitable access that need bridging to harness the Metaverse's full potential. It underscores the necessity for robust technological infrastructure, innovative teaching methodologies for 3D learning environments, and inclusivity strategies for diverse socioeconomic and physical ability spectra. The inclusion of Chuo University's advancements extends the discourse beyond ASEAN, indicating the global momentum of Metaverse adoption in education. The paper posits that ASEAN's venture into this digital frontier, alongside international collaborations, prepares the ground for a more interconnected and technologically advanced future. This investigation into the ASEAN region and its collaboration with Japanese innovation underscores a unique global position, suggesting that the convergence of technology and education is key to resolving deep-rooted disparities in educational access and quality.

关键词： Industries Technological innovation Three-dimensional displays Metaverse Education Collaboration International collaboration

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Advanced eHealth with Explainable AI: Secured by Blockchain with AI-Empowered Block Sensitivity for Adaptive Authentication

Advanced eHealth with Explainable AI: Secured by Blockchain ...

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IEEE International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC)

作者： Joy Dutta Hossien B. Eldeeb Tu Dac Ho Department of Computer Science C2PS Research Center Khalifa University Abu Dhabi UAE Electrical Engineering Division University of Cambridge Cambridge UK Faculty of Information Technology and Electrical Engineering Norwegian University of Science and Technology (NTNU) Trondheim Norway

ISBN: (数字)9798350362244

ISBN: (纸本)9798350362251

This paper presents an innovative, yet secure, eHealth framework that leverages Explainable Artificial Intelligence (XAI) and blockchain technology to enhance transparency and security in the IoT-edge-cloud continuum. The framework incorporates SHapley Additive exPlanations (SHAP) to provide real-time, model-agnostic explanations for AI predictions, enabling personalized health monitoring and informed decisionmaking in healthcare. To strengthen data security, a consortium blockchain is employed, and AI is utilized to identify block data sensitivity at the edge within blockchain-integrated IoT architectures using Random Forest (RF) algorithm. This approach achieves high accuracy in validating block sensitivity, enabling efficient selection of authentication mechanisms in the proof of authentication (PoAh) consensus within the consortium blockchain. This ensures heightened protection for sensitive data and contributes to improved overall blockchain performance. The proposed framework is evaluated in an edge computing environment and demonstrates significant potential for advancing security and authentication in eHealth, representing a substantial advancement in healthcare technology.

关键词： Technological innovation Sensitivity Explainable AI Data security Scalability Authentication Electronic healthcare Consensus protocol Reliability Protection

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Automated Model-Based Test Case Generation Using Uml Activity Diagram: A Review 9th

Automated Model-Based Test Case Generation Using Uml Activit...

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9th International Conference on IT Convergence and Security, ICITCS 2021

作者： Nor, Rozi Nor Haizan Monim, Md Abdul Jusoh, Yusmadi Yah Tajuddin, Nur Ilyana Ismarau Department of Software Engineering and Information System Faculty of Computer Science and Information Technology Universiti Putra Malaysia Serdang Malaysia Tamhidi Centre Universiti Sains Islam Malaysia Nilai Malaysia

ISBN: (纸本)9789811641176

Software or application testing is a process of executing a program with the goal of finding defect to make better system. In software testing phase, writing test cases is one of the important activities. Manually writing test cases approach is lengthy of time period and need more effort to accomplish the process. This paper describes test case, test case generation techniques, different types of software testing approaches and comparison of testing tool. Test cases usually writing at the beginning of testing process from the set of software requirements. Test cases are created by using two different approaches. One is manually written test cases and another is automatically generated test cases. Manually written test cases are a very lengthy process and need to give a lot of effort to make good quality test cases. On the other hand, automatically generated test cases are generated by automatically using some software tools and it saves a lot of time and effort. © 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Software testing

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Developing a Framework for Utilizing AI for Data Access Optimization

Developing a Framework for Utilizing AI for Data Access Opti...

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Smart Generation Computing, Communication and Networking (SMART GENCON), International Conference on

作者： Nagaraju Bogiri Sanjay Kumar Sinha Ashmeet Kaur Vaishali Singh Dimple Bahri X.Mercilin Raajini Department of Computer Engineering Vishwakarma Institute of Information Technology Pune India Department of Computer Science & Engineering Vivekananda Global University Jaipur India Centre of Interdisciplinary Research in Business and Technology Chitkara University Institute of Engineering and technology Chitkara University Punjab India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Department of Civil Engineering Faculty of Engineering and Technology JAIN (Deemed-to-be University) Ramnagar Karnataka India Department of Electronics and Communication Engineering Prince Shri Venkateshwara Padmavathy Engineering College Chennai

The use of artificial intelligence for data access optimization is becoming increasingly important in the field of data science. AI algorithms can be used to create complex data structures that can be used to facilitate faster and more efficient access to data. Additionally, AI can be used to create frameworks capable of handling large amounts of data and optimizing the speed and accuracy of query results. This paper presents a framework for utilizing AI for data access optimization. In this framework, AI algorithms are used to create a structure for data storage and user interfaces that allow users to interact with the data more efficiently. Data mining techniques are used to identify patterns in the data that can be used to generate optimized queries. Additionally, the framework includes tools and libraries to automate processes and enable easy implementation of the developed framework. The proposed framework has been evaluated using a case study, and results show that the AI-based query optimization proposed in this framework can significantly reduce the query result time compared to traditional methods.

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PAPR Reduction Techniques for MC-CDMA System

PAPR Reduction Techniques for MC-CDMA System

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IEEE Annual Computing and Communication Workshop and Conference (CCWC)

作者： Moftah Ali Raveendra K. Rao Vijay Parsa Engineering Technology Department Northwestern State University of Louisiana Natchitoches LA USA Dept. of Electrical and Computer Engineering Faculty of Engineering Innovation Centre for Information Engineering (ICIE) University of Western Ontario London ON Canada

A complex pseudo-random (PN) sequence is proposed and used in a Multi-carrier Code Division Multiple Access (MC-CDMA) system. The correlation properties of this proposed PN-sequence are examined, illustrated, and compared with the well-known Walsh and Gold sequences. The Peak-to-Average Power Ratio (PAPR) properties of signals in an MC-CDMA system with complex PN-sequence are examined. Also, techniques such as clipping based on symbol statistics and improved Partial Transmit Sequence (PTS) are used for PAPR reduction in the MC-CDMA system. Numerical results show that the proposed complex PN-sequence exhibits good correction properties and the PAPR performance of MC-CDMA system is a function of the PN-sequence and the PAPR reduction method used. It is shown that significant PAPR reduction can be attained using the clipping based on symbol statistics technique and it is controlled by the value of the clipping factor (a) used in the system. In addition, when the improved PTS technique is applied for PAPR reduction in the MC-CDMA system, a PAPR reduction of nearly 4. 6 dB can be achieved compared to the system without this technique.

关键词： Multicarrier code division multiple access Gold Correlation Partial transmit sequences Conferences Symbols Peak to average power ratio

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