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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Chen, Yijia Chen, Yiwen Nie, Shanling Yang, Hai East China University of Science and Technology Department of Computer Science and Engineering Shanghai China Shanghai Key Laboratory of Computer Software Evaluating and Testing Shanghai China National University of Singapore Center for Continuing and Lifelong Education Singapore The University of Sydney Faculty of Engineering Australia

ISBN: (纸本)9798350337488

Genomic variants, which can disrupt cellular functions, present a challenge in distinguishing deleterious from benign instances. While assessing genome-wide functional impacts, many current algorithms neglect protein tertiary structure of coding region variants due to limitations in protein structural prediction. This study introduces PMMVar, an advanced multimodal deep convolutional network, which adeptly integrates protein tertiary structures with conservation properties from ESM-2, supplemented by other protein structural sequences. PMMVar achieves outstanding performance on the latest clinical variant datasets, NCBI ClinVar (2023), and the Mendelian variant dataset, surpassing existing benchmarks. Ablation analyses validate the significance of protein multi-level structures in enhancing the model's accuracy. Overall, our findings spotlight the essential role of multi-level protein structures in pathogenicity predictions and their potential to discern deleterious genomic variants effectively. © 2023 IEEE.

关键词： deep learning multimodal pathogenicity protein structure variant

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Iot monitoring lathe machine performance

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Materials Today: Proceedings 2023年 80卷 3570-3574页

作者： Sharma, Rupak Sharma, Rajeev Prabha, Y.B. Rema Devi, S. Saxena, Pankaj Rajasanthosh kumar, T. Department of Computer Applications Faculty of Science& Humanities SRM Institute of Science & Technology NCR Campus Delhi NCR Campus Meerut-Delhi Road Modinagar UP Ghaziabad India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Guntur Vaddeswaram India Department of English School of Liberal Arts and Special Education Kalasalingam Academy of Research and Education Krishnankoil Virudhunagar District India R.B.S. Management Technical Campus Agra India Department of Mechanical Engineering Oriental Institute of Science & Technology Bhopal India

Machines have to operate effectively and free from faults in order to enhance productivity, increase quality, and decrease costs. With automated and flexible action, this is far more likely. An accurate identification of the underlying cause Causes of failure that need repairs and the time for restoration include: A lot of Various monitoring methods are available to track the experimentally-recorded machine health. Maintaining instruments and sensors. The vibration monitoring methods in this list. It is the right fit, and it has shown itself over time. Measuring vibrations in this job three different locations resembling the stock of a gun: headstock, tailstock. When conducting turning operation with mild steel, the tool post of the lathe machine is made of metal. Data that is gathered through sensors is sent straight to the cloud in this case, such as Ubidots IoT platform. via way of nodeMCU. Whether a task is impacted or a tool is impacted, mission failure has occurred. Thus, in either instance, early warning is provided by a signature, thus the development does not progress as quickly machining. If the tail-stock seems to be broken, and this appears to be a consistent problem, then the whole batch must be inspected for errors to be inspected closely is anything that has been created under such circumstances. An optimal decision-making procedure light may be thrown on CM so processes may be machine-wise instituted in a shop there. Tools while talking about lathes that are large, medium, or tiny. Overall it is a method which is easy but yet capable of benefitting people at all levels, from the absolute beginner to experts Floor productivity is aided by training elements that are almost unnecessary. © 2021

关键词： Lathes

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Car Price Prediction using Machine Learning 9

Car Price Prediction using Machine Learning

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9th IEEE International Conference for Convergence in Technology, I2CT 2024

作者： Ahmad, Muhammad Farooq, Muhammad Ali Hussain, Muhammad Zunnurain Hasan, Muhammad Zulkifl Mustafa, Muzzamil Khalid, Aqsa Awan, Rimsha Hussain, Usman Khan, Zohaib Ahmed Javaid, Arslan University Lahore Department of Computer Engineering Information Technology Punjab Pakistan Bahria University Lahore Campus Zunnurain Dept. of Computer Science Pakistan University of Central Punjab Lahore Faculty of Information Technology Department of Computer Science Pakistan National College of Business Administration And Economics Department of Computer Science Lahore Pakistan Information Technology University Lahore Pakistan University of Home Economics Pakistan

ISBN: (纸本)9798350394474

Predicting car prices involves determining a vehicle's market worth based on various attributes like brand, model, year of manufacture, mileage, and overall state. This prediction holds significant value for the auto industry, aiding both prospective buyers and sellers in understanding pricing and making educated decisions regarding vehicle transactions. Machine learning techniques, trained on historical datasets sourced from online automotive marketplaces, dealerships, and auction platforms, are adept at forecasting the prices of both new and pre-owned cars, using their specifications. Noteworthy machine learning models applied include Linear Regression, AdaBoostRegressor, Lasso Regression, and Ridge Regression. Integrating more diverse data points, such as customer reviews, prevailing market dynamics, and regional factors, can further refine the accuracy of these prediction models. In recent trials with the provided dataset, the Lasso Regression and Ridge Regression models stood out, delivering an impressive accuracy rate nearing 90%. © 2024 IEEE.

关键词： Forecasting

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systems for Remote Access to FPGA Development Boards: Review 31

Systems for Remote Access to FPGA Development Boards: Review

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31st International Scientific Conference Electronics, ET 2022

作者： Minev, P. Kukenska, V. Varbov, I. Dinev, M. Technical University of Gabrovo Faculty of Electrical Engineering and Electronics Department of Computer Systems and Technology Gabrovo5300 Bulgaria

ISBN: (数字)9781665498784

ISBN: (纸本)9781665498784

This report presents the study results of remote access systems for FPGA development hardware during the period 2008-2021. The possibilities and development of these systems are considered. The study made it possible to define the requirements and the expected final results in the implementation of a new system for remote access to FPGA development hardware, which is in the process of implementation in the 'Automated design' laboratory at the KST department of the Technical University-Gabrovo. © 2022 IEEE.

关键词： Field programmable gate arrays (FPGA)

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Factored Task and Motion Planning with Combined Optimization, Sampling and Learning

arXiv

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arXiv 2024年

作者： Ortiz-Haro, Joaquim TU Berlin Faculty IV - Electrical Engineering and Computer Science Learning and Intelligent Systems Germany

Modern robots excel at performing simple and repetitive tasks in controlled environments;however, future applications, such as robotic construction and assistance, will require long-term planning of physical interactions. These problems can be formulated as Task and Motion Planning (TAMP). The goal is to find how the robot should move to solve complex tasks requiring multiple interactions with objects in the environment, such as building furniture or cleaning and organizing the kitchen. However, TAMP is notoriously difficult to solve because it involves a tight combination of task planning and motion planning, considering geometric and physical constraints. In this thesis, we aim to improve the performance of TAMP algorithms from three complementary perspectives. First, we investigate the integration of discrete task planning with continuous trajectory optimization. Our main contribution is a conflict-based solver that automatically discovers why a task plan might fail when considering the constraints of the physical world. This information is then fed back into the task planner, resulting in an efficient, bidirectional, and intuitive interface between task and motion, capable of solving TAMP problems with multiple objects, robots, and tight physical constraints. Traditionally, there have been two competing approaches to solving TAMP problems: sample-based and optimization-based methods. In the second part, we first illustrate that, given the wide range of tasks and environments within TAMP, neither sampling nor optimization is superior in all settings. To combine the strengths of both approaches, we have designed meta-solvers for TAMP, adaptive solvers that automatically select which algorithms and computations to use and how to best decompose each problem to find a solution faster. A third promising direction to improve TAMP algorithms is to learn from previous solutions to similar problems. In the third part, we combine deep learning architectures with model-base

关键词： Motion planning

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A Deep Convolution Network-Based Pneumonia Identification from Thoracic X-Ray Imagery Scans 6th

A Deep Convolution Network-Based Pneumonia Identification fr...

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6th World Conference on Smart Trends in systems, Security and Sustainability, WS4 2022

作者： Upreti, Kamal Mishra, Prateet Maheshwari, Manish Vats, Prashant Dhankar, Aisha Batra, Reenu Yadav, Saneh Lata Chawla, Tanvi Bhardwaj, Jayant Department of Computer Science and Engineering Dr. Akhilesh Das Gupta Institute of Technology and Management Delhi New Delhi India Department of Computer Science and Information Technology Symbiosis University of Applied Sciences MP Indore India Department of Computer Science and Applications Makhanlal Chaturvedi National University of Journalism and Communication MCNUJC Madhya Pradesh Bhopal India Department of Computer Science and Engineering Faculty of Engineering and Technology SGT University Haryana Gurugram India Department of Electronics and Communication Bhagwan Parshuram Institute of Technology GGSIPU New Delhi India

ISBN: (纸本)9789811976599

Pneumonia is an infectious ailment which affects the respiratory system and is caused by microbes that infect the human by affecting their lung air sacs and fill them with fluid. Using chest X-ray is the most popular approach for detecting pneumonia, and the results must be calculated and examined by medical experts. The inconvenient way of detecting pneumonia results in a loss of human life owing to incorrect approach and treatment. With the advancement of computer and information technology, developing an autonomous system that can help in the diagnosis and prevention of pneumonia is now viable, especially if the patient is in a place where the medical facilities and expert doctors are limited. In order to solve this challenge, this project uses deep learning technologies. The neural network was customized to accomplish the difficult task of detecting diseases such as pneumonia in order to aid medical experts in their diagnosis and treatment options. CNN applies a filter to a photo in order to extract data from a chest X-ray. The above-mentioned goal was achieved using Python programming language, as well as deep learning and neural network methodologies. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Chest X-rays Deep CNN Recognition of pneumonia Rectified linear function Thoracic X-ray component

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Machine Learning based Intelligent Diagnosis of Orthodontics: A comprehensive review 3

Machine Learning based Intelligent Diagnosis of Orthodontics...

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3rd IEEE International Conference on Computational Intelligence and Knowledge Economy, ICCIKE 2023

作者： Iwin Thanakumar Joseph, S. Sarveshwaran, Velliangiri Vijayakumar, K. Raj, S Benson Edwin Elngar, Ahmed A Devadurai, Anand Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Andhrapradesh Guntur India SRM Institute of Science and Technology Kattankulathur Campus Department of Computer Science and Engineering Chennai India Higher Colleges of Technology Department of Computer Information Sciences Fujairah United Arab Emirates Beni-Suef University Faculty of Computers and Artificial Intelligence Beni-Suef City Egypt King Khalid University Department of Computer Science Gregar Abha Saudi Arabia

ISBN: (纸本)9798350338263

An effective prediction of orthodontics treatment outcome is highly essential for further scheme of clinical treatment for the patient with the simplified cost. An appropriate utilization of information technology simplifies the treatment for teeth irregularities and misaligned jaws in an effective manner. Lot of research works carried out in the area of dentistry to come out with an optimistic result. With the advent of artificial intelligence and machine learning methodologies in recent years, lot of clinical problems and difficulties can be efficiently managed and overcome by orthodontists. This research article provides the complete insight about the impact of intelligent technologies in the field of orthodontics. It provides a complete overview of futuristic applications in the field of orthodontic clinical diagnosis, planning of treatment for various cases and the correct prediction of outcome of clinical treatment which regularly faced by an orthodontist in the health care sector. © 2023 IEEE.

关键词： Dentistry

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Empirical Literature on Economic Growth, 1991–2020: Uncovering Extant Gaps and Avenues for Future Research

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Global Journal of Emerging Market Economies 2023年第1期15卷 7-37页

作者： Doré, Natalia I. Teixeira, Aurora A. C. Centro de Economias e Finanças da UP (CEF.UP) Faculty of Economics University of Porto Porto Portugal Institute for Systems and Computer Engineering Technology and Science (INESC TEC) Porto Portugal

The factors required to achieve sustainable economic growth in a country are debated for decades, and empirical research in this regard continues to grow. Given the relevance of the topic and the absence of a comprehensive, systematic literature review, we used bibliometric techniques to examine and document several aspects in the empirical literature related to growth, from 1991 to 2020. Five main results are worth highlighting: (a) the share of empirical articles on economic growth show a clear upward trend;(b) among all the groups of countries considered, the emerging economies (EEs) have received the most scientific attention;(c) the economic growth processes of the Latin American and Caribbean EEs have observed negligible scientific attention;(d) the very long-run studies comprise a residual share among the empirical literature on growth;(e) the extant empirical studies on economic growth have addressed mainly the impact of “macroeconomic conditions.” Our findings suggest there is a need to redirect the empirical growth agenda, so as to encourage more scientific attention devoted to the analysis of key determinants of economic growth in the very long run. There should also be increased scrutiny of the processes of economic growth in Latin American and Caribbean EEs © 2021 Emerging Markets Forum, Washington DC.

关键词： bibliometric Economic growth emerging economies systematic survey

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Augmented Reality in Higher Education: Literature Review 25

Augmented Reality in Higher Education: Literature Review

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25th International Seminar on Intelligent Technology and Its Applications, ISITIA 2024

作者： Maulana, Fairuz Iqbal Rahayu, Anita Zamahsari, Gamal Kusuma Adi, Puput Dani Prasetyo Arifuddin, Rahman Dirgantara, Wahyu Bina Nusantara University School of Computer Science Computer Science Department Jakarta11480 Indonesia Bina Nusantara University Faculty of Humanities Digital Language Learning Center Communication Science Department Jakarta11480 Indonesia Research Center for Telecommunication Bandung Indonesia University of Merdeka Malang Department of Electrical Engineering Malang Indonesia

ISBN: (纸本)9798350378573

Augmented Reality (AR) is rapidly transforming educational landscapes, offering an immersive and interactive learning experience. This literature mapping review explores the current landscape of AR in higher education research. By systematically analyzing peer-reviewed publications in the last five years from 2019 to 2023, the review identifies key trends, application areas, author collaboration patterns, and research foci, were extracted, analyzed, and visualized using R and VOSViewer. The analysis reveals a growing body of research, with a surge in publications after 2020. science, Technology, engineering, and Mathematics (STEM) subjects dominate AR applications, highlighting its potential to visualize abstract concepts. The review further investigates the impact of AR on student outcomes, including engagement and learning processes. It also examines the geographical distribution of research and identifies knowledge gaps for future exploration. Overall, this review provides a comprehensive map of AR in higher education research, laying the groundwork for further investigation into its pedagogical effectiveness and broader implementation across disciplines. © 2024 IEEE.

关键词： Adversarial machine learning

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Performance of Adaptive Neuro-Fuzzy Inference System State-of-Charge Estimation of Lithium-Ion Batteries for Electric Ship 8

Performance of Adaptive Neuro-Fuzzy Inference System State-o...

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8th International Conference on Computational Intelligence and Applications, ICCIA 2023

作者： Marisno, Denny Chin, Cheng Siong Shan, Chunlai See, Simon Newcastle University in Singapore Faculty of Science Agriculture Engineering Singapore Northwest Institute of Mechanical and Electrical Engineering Simulation and Test Technology Department Xianyang China Shanghai Jiao Tong University 800 Dongchuan RD. Minhang District Department of Computer Science and Engineering Shanghai200240 China

ISBN: (纸本)9798350339741

Estimating the State of Charge (SoC) is a crucial part of a battery management system (BMS) for the electric ship. An Adaptive Neuro-Fuzzy Inference System (ANFIS) to predict the SoC using the ANFIS under different ambient temperature variations is proposed. A five-layer Artificial Neural Network for SoC estimation is used. The ANFIS achieves the lowest prediction error compared to the Neural Network, electrical circuit model, coulomb counting, Kalman Filter, Fuzzy Neural Network, and Support Vector Machine method. © 2023 IEEE.

关键词： Battery management systems

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