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Advances in Intelligent Computing and Applications (AICAPS), International Conference on

作者： K. Banumathi G. SudhaSadasivam B BanuRekha N. Shahana K. Vaishnavi Director of PSG Vishnugranthi School for Special Children Coimbatore India Dept. Computer Science and Engg PSG College of Technology Coimbatore India Dept of Biomedical Engg PSG College of Technology Coimbatore India

Dyslexia is a learning disorder characterized with a difficulty in learning due to impairment of the left hemisphere of the brain associated with processing. In India, the incidence of dyslexia is believed to be around 15%. Literacy is the foundation of all learning and hence, identifying dyslexia at an early age is critical. The appropriate age to identify dyslexia is between 5 and 8 years since early detection can nurture corrective measures. Identifying dyslexia at this age will also prevent future school drop outs. The major challenge in detecting dyslexia among children at an early age includes lack of awareness among school teachers and parents. The absence of simple standardized screening and assessment tools in regional languages makes the task more difficult. A Smartphone application to screen dyslexia offers the advantages of universal use and standardization. However, primary school educators feel the need to have screening apps in regional languages (like Tamil), as most of the communication in local population is using regional language. In this research work, a Smartphone based screening application for dyslexia is developed in Tamil. The app consists of questions that cover the areas of general qualities, health & personality, unique talents, speech & hearing skills, visual acuity& reading skills, writing & motor skills, mathematical ability and memory & cognitive skills. The app was created utilizing the expertise of special educators and following regional practices. The app generates visualizations and provides scoring on the severity level of dyslexia for the user. The report can be downloaded and printed. Then the dataset is subjected to training with five different machine learning algorithms and results are compared by their report of classification and error rates.

关键词： Training Visualization Error analysis Education Sociology Standardization Writing

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Sustainable AI & Agricultural Technologies

Sustainable AI & Agricultural Technologies

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Autonomic Computing and Self-Organizing Systems Companion (ACSOS-C), IEEE International Conference on

作者： Ṣtefan Sarkadi Ionuţ Moraru Louise Manning Dept. of Informatics King's College London London UK School of Computer Science University of Lincoln Lincoln UK Lincoln Institute for Agri-food Technology University of Lincoln Lincoln UK

Climate change, increase in world population and the war in Ukraine have led nations to put a larger focus on food security, while simultaneously trying to halt declines in biodiversity and reduce risks to human health posed by chemically-reliant farming practices. Achieving these goals simultaneously will require the adoption of novel approaches. One potential solution is accelerating the uptake of AI & Robotic Technologies in the agricultural sector. However, we currently do not have a scientific model for predicting the effects of adopting AI-based agri-tech in complex systems such as the food sector. Self-organising systems offer a holistic and intuitive perspective on complex ecosystems and their resilience to socio-economic pressures at various scales. We propose the application of evolutionary agent-based modelling to explore the emergence of strategies in the food sector considering various levels of abstraction.

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An Automated Detection of Amyotrophic Lateral Sclerosis from Resting-State MEG Data Using 3D Deep Convolutional Neural Network

An Automated Detection of Amyotrophic Lateral Sclerosis from...

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2023 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2023

作者： Samanta, Kaniska Roy, Sujit Marchand-Pauvert, Veronique Dora, Shirin Duguez, Stephanie Singh, Muskaan Prasad, Girijesh Bhattacharyya, Saugat Ulster University Intelligent System Research Center United Kingdom Brainalive Research Pvt. Ltd. Kanpur208001 India Sorbonne Universités Upmc Univ Paris 06 Cnrs Inserm Laboratoire d'Imagerie Biomédicale France Loughborough University Dept. of Computer Science United Kingdom School of Biomedical Sciences Ulster University United Kingdom

ISBN: (纸本)9798350337020

A novel 3D deep convolutional neural network (3D-CNN) model called MEGNet3D has been proposed in the paper. MEGNet3D is designed to differentiate between amyotrophic lateral sclerosis (ALS) and healthy individuals from their resting state (eyes open and eyes closed condition) sensor-level magnetoencephalography (MEG) data. The raw MEG data is initially transformed into their time-frequency representation, which are then used as inputs to MEGNet3D. Both magnetometer and gradiometer recordings have been investigated separately. The proposed model exhibits an accuracy of over 75% for most classification conditions. Thus, MEGNet3D is capable of handling high subject variability and shows that spectral-temporal representation of resting-state MEG data yields relevant neural markers related to the existence of ALS. Furthermore, it has also been observed resting state with eyes closed yields better classification accuracy as compared to the resting state with eyes open condition. © 2023 IEEE.

关键词： Amyotrophic lateral sclerosis (ALS) deep learning magneto encephalography (MEG) vision transformer

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Design and Implementation of 2-DOF Arm Manipulator Robot as Robotic Kinematic Learning Platform

Design and Implementation of 2-DOF Arm Manipulator Robot as ...

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Vocational Education and Electrical Engineering, International Conference on

作者： Parama Diptya Widayaka Pradini Puspitaningayu Sayyidul Aulia Alamsyah Rifqi Firmansyah Muktiadji Arif Widodo Rifqi Abdillah Dept. of Electrical Engineering Universitas Negeri Surabaya Surabaya Indonesia Dept. of Electrical and Computer Engineering King Abdulaziz University Jeddah Saudi Arabia Graduate school of Interdisciplinary Science and Engineering in Health Systems Okayama University Okayamas Japan Dept. of Informatics Institut Teknologi Sepuluh Nopember Surabaya Surabaya Indonesia

Technological developments have impacts and challenges in human life. Technological advances have made human life easier. The impact of technological developments is the industrial manufacturing process getting faster with the help of robotics technology. Robots can shorten manufacturing process time and efficient. On the other hand, challenges with technological developments force us to keep learning and adaptive to every changes around us, especially in the education sector. In education sector, formal educational institutions or campuses must be able to facilitate students to be able to learn comprehensively in the field of technology, especially robotics, where we can make an innovative learning platform based on robotics technology. In this study, we discussed a 2-DOF arm manipulator robot as kinematics learning platform of industrial robot to increase student competencies in preparation for facing the industrial environment in century. Inverse kinematics equation has been applied in the robot by providing input coordinates on the x-axis and y-axis where the accuracy value on the x-axis is 77% and the accuracy on the y-axis is 83.1%.

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Real-Time Person Re-Identification to Improve Human-Robot Interaction

Real-Time Person Re-Identification to Improve Human-Robot In...

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IEEE International Conference on Real-time Computing and Robotics (RCAR)

作者： Per Antoine Carlsen Angelique M. Taylor Darren M Chan Md Zia Uddin Laurel Riek Jim Torresen Logistics Oda Oslo Norway College of Computing and Information Science Cornell Tech Cornell University New York USA HRL Laboratories LLC Malibu CA USA Sustainable Communication Technologies SINTEF Digital Oslo Norway Dept. of Computer Science and Engineering University of California San Diego USA Dept. of Informatics and RITMO University of Oslo Oslo Norway

ISBN: (数字)9798350372601

ISBN: (纸本)9798350372618

Mobile robots deployed in the homes of disabled people and older adults can play a vital role as companions and aid in activities of daily living, enhancing people’s quality of life. For these robots to effectively assist, it can be helpful if they possess social and interactive skills to facilitate natural and personalized interactions. Personalization, including using proper names and remembering preferences, is crucial for building strong social connections and fostering trust in human-robot interaction. However, achieving personalized interactions necessitates the robot’s ability to autonomously recognize and re-identify individuals without relying on specific constraints or prior knowledge. This work introduces two lightweight Dual convolutional neural networks, LuNet Light and LuNet Lightest, designed for person re-identification in a robotic context without the limitations of existing systems. We introduce a lightweight architecture, LuNet Lightest which achieves near state-of-the-art performance on the MARS dataset. It is well-suited for real-time applications on low-cost, hardware-constrained robots. This person-identification system enables assistive mobile robots to accurately identify users, fostering trust and facilitating natural, personalized interactions.

关键词： Measurement Human-robot interaction Network architecture People with disabilities Real-time systems Mobile robots Convolutional neural networks

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Technical Debt Management: The Road Ahead for Successful Software Delivery

arXiv

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arXiv 2024年

作者： Avgeriou, Paris Ozkaya, Ipek Chatzigeorgiou, Alexander Ciolkowski, Marcus Ernst, Neil A. Koontz, Ronald J. Poort, Eltjo Shull, Forrest Dept. of Computing Science University of Groningen Groningen Netherlands Software Engineering Institute Carnegie Mellon University PittsburghPA United States Dept. of Applied Informatics University of Macedonia Thessaloniki Greece QAware GmbH München Germany Department of Computer Science University of Victoria Victoria Canada The Boeing Company MesaAZ United States CGI Rotterdam Netherlands

Technical Debt, considered by many to be the 'silent killer' of software projects, has undeniably become part of the everyday vocabulary of software engineers. We know it compromises the internal quality of a system, either deliberately or inadvertently. We understand Technical Debt is not all derogatory, often serving the purpose of expediency. But, it is associated with a clear risk, especially for large and complex systems with extended service life: if we do not properly manage Technical Debt, it threatens to "bankrupt" those systems. Software engineers and organizations that develop softwareintensive systems are facing an increasingly more dire future state of those systems if they do not start incorporating Technical Debt management into their day to day practice. But how? What have the wins and losses of the past decade of research and practice in managing Technical Debt taught us and where should we focus next? In this paper, we examine the state of the art in both industry and research communities in managing Technical Debt;we subsequently distill the gaps in industrial practice and the research shortcomings, and synthesize them to define and articulate a vision for what Technical Debt management looks like five years hence. © 2024, CC BY-NC-ND.

关键词： Industrial research

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Selective Attention UNet for Segmenting Liver Tumors

Selective Attention UNet for Segmenting Liver Tumors

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Cyber-Physical Social Intelligence (ICCSI), International Conference on

作者： Darshan Patil Gopal Sakarkar Pearl Khatri Atharva Khedkar Hong-Seng Gan Muhammad Hanif Ramlee Dept. of Artificial Intelligence G. H. Raisoni College of Engineering Nagpur India School of Computer Science Dr. Vishwanath Karad MIT world peace University Kothrud Pune Dept. of Computer Science Banasthali Vidyapith Jaipur India School of AI and Advanced Computing Xi’an Jiaotong-Liverpool University Suzhou China Dept. of Biomedical Engineering Universiti Teknologi Malaysia Johor Malaysia

In order to segregate liver tumours in medical imaging applications, a novel architecture called Selective Attention UNet is proposed in this article. The suggested architecture, which is based on the well-known UNet architecture, has a selective attention module that enables the network to concentrate on crucial tasks while suppressing unnecessary ones. Link skipping between the encoder and decoder routes is another element of the design that enables the network to effectively segment data using both low-level and high-level attributes. On the publicly accessible LiTS dataset, we assessed the performance of the suggested architecture and contrasted it with four fundamental models: FCN, UNet, UNet++, and SegNet. The Dice Similarity Coefficient (DSC) of 0.89 a mean IOU of 0.76 obtained in our experiments demonstrates that the suggested architecture beats all baseline models in terms of accuracy and robustness criteria. The project is accessible at: https://***/darshan8850/Liver-tumor-Segmentation

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Topic Relation Graph Construction to Identify Challenges in Introducing Programming in Indonesian K-12 Education

Topic Relation Graph Construction to Identify Challenges in ...

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Smart Technology (ICE-SMARTec), IEEE International Conference on

作者： Mewati Ayub Oscar Karnalim Hapnes Toba Maresha Caroline Wijanto Hans Dulimarta Williyandie Saputra Tabitha Emanuella Kotambunan Master Program of Computer Science Maranatha Christian University Bandung Indonesia Dept. of Computer Science and Engineering Ewha Womans University Seoul South Korea School of Computing Grand Valley State University Michigan USA Informatics Engineering Maranatha Christian University Bandung Indonesia

Programming can help K-12 students to develop their 21st-century core skills. Despite the benefits, programming is not common to be delivered in Indonesian K-12 education. There is a need to understand potential challenges in introducing programming to K-12 students. We developed a questionnaire survey covering four identified dimensions of challenges: administrative, facilities, teachers, and students. We also asked about common programming assessments and their preferred software features for teaching programming. Forty K-12 teachers were invited to complete the survey. The responses were analyzed with thematic analysis using a bigram-based Latent Dirichlet Allocation topic modeling and descriptive statistics. Our study shows that the challenges include limited learning modules, an insufficient number of computers, limited programming skills, and limited computational thinking skills. Scratch was the most common programming language used and many programming assessments were about debugging a program or writing a small program. Visualization and animation can be helpful in teaching programming.

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Converging for Security: Blockchain, Internet of Things, Artificial Intelligence - Why Not Together?

Converging for Security: Blockchain, Internet of Things, Art...

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International Conference on computer Applications and Industrial Electronics (ICCAIE)

作者： Ahmad Anwar Zainuddin Dini Handayani Isyraq Haziq Mohd Ridza Siti Husna Abdul Rahman Saidatul Izyanie Kamarudin Khairul Zakwan Ahmad Mirza Darwisy Mahazir Muazzam Hazmi Sukhaimi Krishnan Subramaniam Mohamad Irfan Firdaus Basri Nurul Hanis Mohd Dhuzuki Dept. of Computer Science KICT International Islamic University Malaysia Gombak Malaysia Dept. of Computer Science KICT International Islamic University Malaysia Kuala Lumpur Malaysia Faculty of Computing Informatics Multimedia University Cyberjaya Selangor Malaysia Kolej Pengajian Pengkomputeran Informatik dan Matematik (KPPIM) Universiti Teknologi MARA Shah Alam Selangor Malaysia Department of Mechanical Engineering Mila University Nilai Negeri Sembilan Malaysia

ISBN: (数字)9798350348798

ISBN: (纸本)9798350348804

Blockchain, renowned for its decentralized and secure nature, and Artificial Intelligence, the pinnacle of machine intelligence, stand as pillars of innovation. However, despite the recognition of their few successful collaborations, the interrelation among these triumvirates - Internet-of-Things (IoT), Blockchain, and Artificial Intelligence (AI) - still remains relatively unexplored terrain. Issues arise regarding their synergy: the challenges of integration, the potential improvements in existing technologies, and the careful consideration required before their unification. While evidence suggests their individual prowess, understanding their convergence and the implications thereof is crucial for shaping a technologically robust future. This paper delves into the complex interplay between IoT, Blockchain, and AI, navigating their relationship and exploring opportunities for enhancement. In addition, this paper will also look into several challenges and complexities hindering seamless integration.

关键词： Ethics Technological innovation Navigation Collaboration Blockchains Complexity theory Stakeholders

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Design a compact wireless IoT gait monitor wearable sensory system

Design a compact wireless IoT gait monitor wearable sensory ...

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2021 International Telecommunications Conference, ITC-Egypt 2021

作者： Abdelhady, Mohamed Elsheikh, Hesham Eldesoky Ahmed, Wael Abouelwafa Cleveland State University Electrical Engineering and Computer Science Dept. ClevelandOH44115 United States Minia University Faculty of Engineering Biomedical Engineering Department Minya Egypt

ISBN: (纸本)9781665445733

The study of human gait analysis is an important endeavor for years, since there are numerous sectors for patients with walking difficulties, such as stroke, sports, and medical rehabilitation. However, such research necessitates the use of equipment that can precisely quantify the movement of body parts. This paper shows a guideline scheme to develop a compact wireless internet of things (IoT) board, starting from embedded system design to kinematic data collection. This work aim is to create an acquisition IoT board that can measure and gather the knee, hip and ankle angles of a person walking normally as well as a patient walking with certain diseases. An enclosure designed to host the board and its associated battery form a wearable unite which it is able to attach to human lower limbs. The electronic board design to establish an inner communication between a high-fidelity inertial measurement unite (IMU) and microcontroller, and an outer communication between microcontroller and radio frequency (RF) module. The board design considers a bidirectional radio communication, though multiple boards are able to share, exchange, and pass IMU data to a personal computer as well. © 2021 IEEE.

关键词： Internet of things

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