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Communication, Computing and Energy Efficient Technologies (I3CEET), International Conference on

作者： Ramy Riad Al-Fatlawy Dadamuxamedov Alimjon R. Udayakumar Ashish Sharma Jaspreet Kaur S. Padmanaban Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq Department of Computers Techniques Engineering College of Technical Engineering The Islamic University of Al Diwaniyah Al Diwaniyah Iraq Senior Lecturer at the Department of "Modern Information and Communication Technologies" of International Islamic Academy of Uzbekistan Tashkent Uzbekistan Dean CS & IT Kalinga University India Department of Computer Engineering and Applications GLA University Mathura India Assistant Professor Department of Computer Science Engineering Chandigarh Engineering College Chandigarh Group of Colleges Jhanjeri Mohali Punjab India Department of Science and Humanities Karpagam Institute of Technology Coimbatore India

ISBN: (数字)9798331541583

ISBN: (纸本)9798331541590

The many real-world applications of machine learning-based approaches over the past several decades have shown the potential of data-driven methodology in a variety of computing fields. Higher education computer curricula are beginning to include machine learning, and more and more institutions are incorporating it into K–12 computer instruction. Given the growing prevalence of computational learning in K–12 computer teaching, it is imperative to investigate how agency and intuition develop in these kinds of settings. However, considering the challenges educators and schools already have in integrating conventional learning, understanding the challenges connected with teaching algorithms for learning across grades K–12 provides an even more challenging barrier for computer education research. the curriculum's incorporation of artificial intelligence and *** article outlines the potential for data mining education in grades K–12. These advancements include modifications to technology, philosophy, and practice. The study puts the present findings in the larger framework of computing education and discusses some differences that K–12 computer educators should keep in mind while tackling this issue. The study focuses on key components of the fundamental shift that is required in order to successfully integrate machine learning into more thorough K–12 computing curricula. A critical first step is to abandon the notion that next-generation computational thinking requires rule-based, "traditional" programming.

关键词： Training Technological innovation Philosophical considerations Education Machine learning Learning (artificial intelligence) Market research Telecommunication computing Programming profession Next generation networking

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Variational inference and bayesian cnns for uncertainty estimation in multi-factorial bone age prediction

arXiv

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arXiv 2020年

作者： Eggenreich, Stefan Payer, Christian Urschler, Martin Štern, Darko Institute of Computer Graphics and Vision Graz University of Technology Austria School of Computer Science University of Auckland New Zealand

Additionally to the extensive use in clinical medicine, biological age (BA) in legal medicine is used to assess unknown chronological age (CA) in applications where identification documents are not available. Automatic methods for age estimation proposed in the literature are predicting point estimates, which can be misleading without the quantification of predictive uncertainty. In our multi-factorial age estimation method from MRI data, we used the Variational Inference approach to estimate the uncertainty of a Bayesian CNN model. Distinguishing model uncertainty from data uncertainty, we interpreted data uncertainty as biological variation, i.e. the range of possible CA of subjects having the same BA. Copyright © 2020, The Authors. All rights reserved.

关键词： Uncertainty analysis

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Publisher Correction to: Leveraging chaos for enhancing encryption and compression in large cloud data transfers

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The Journal of Supercomputing 2024年第9期80卷 13493-13494页

作者： Bhattacharjee, Shiladitya Sharma, Himanshi Choudhury, Tanupriya Abdelmoniem, Ahmed M. School of Computer Science University of Petroleum and Energy Studies (UPES) Dehradun India CSE Department IIT Jodhpur Jodhpur India CSE Dept Symbiosis Institute of Technology Symbiosis International (Deemed University) Pune India Professor (Research) CSE Dept. Graphic Era Deemed to be University Dehradun India School of EECS Queen Mary University of London London UK Faculty of Computers and Information Assiut University Assiut Egypt

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Cloud-Based Smart Sensing System for Real-Time Health Management of Dementia Patients

Cloud-Based Smart Sensing System for Real-Time Health Manage...

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International Conference on Smart Technologies For Smart Nation (SmartTechCon)

作者： R. Latha R. Ramya U. Arul Nilamadhab Mishra S. Dhanalakshmi Professor Department of Computer Science and Engineering K. Ramakrishnan College of Engineering Trichy Tamil Nadu India Department of Mathematics S.A Engineering College Chennai Tamil Nadu India Professor Department of Artificial Intelligence and Machine Learning Saveetha School of Engineering Saveetha Institute of Medical And Technical Sciences Chennai Tamil Nadu School of Computing Science and Engineering VIT Bhopal University Madhya Pradesh India Assistant Professor Department of Electronics and Communication Engineering Sri Muthukumaran Institute of Technology Chennai Tamil Nadu India

The paper describes an innovative technique of monitoring updates on the health of people with dementia by integrating smart sensing technologies with a cloud-based platform. Because dementia is a degenerative neuropsychiatric disease that develops with time, constant patient monitoring is essential for ensuring the health and safety of those living with the condition. The proposed system uses smart sensing devices, including wearable and ambient sensors, to monitor dementia patients' health in real-time. Data like heart rates, movements used, and ambient conditions are collected and sent to a server in the cloud for analysis. The camera can monitor and analyze visual data, capture crucial information, and help evaluate the patient's well-being. Multiple patients can be monitored effectively in real time because of the cloud-based system's scalability, accessibility, and storage capacities. For healthcare providers to recognize irregular movements, identify possible risks to health, and implement preventative measures, doctors utilize the moment data analytics devices to gather valuable insights from the acquired data. In addition, the cloud-based solution improves collaborative care and enhances patient support by allowing for continuous communication and information sent among caregivers, doctors, nurses, and family members. Predefined thresholds or anomalous occurrences may trigger alerts and messages, allowing for quick action in reaction to emergencies. Modern technology and data analytics increase the health and happiness of people with dementia.

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IoT-based shrewd crop field MPA system

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AIP Conference Proceedings 2025年第1期3224卷

作者： Himanshu Agarwal Shweta Agarwal Utkarsh Mishra Vanshika Chaudhary Vanshita Singh Shaurya Chauhan Associate Professor Department of Computer Science & Engineering Moradabad Institute of Technology Moradabad (U.P.)-India Assistant Professor Department of Mathematics Ramabai Ambedkar Government Degree College Gajraula Amroha (U.P.)-India UG Scholars Department of Computer Science & Engineering Moradabad Institute of Technology Moradabad (U.P.)-India

Context: Agriculture stands as a pivotal driver of economic progress within a nation, yet the realm of technical advancements within this sector remains distressingly neglected by a multitude of governments. While farmers contribute untiring efforts to tend to their fields, substantial time is squandered on tasks like irrigation and safeguarding crops from birds and animal threats [1, 2]. This unwavering dedication often exacts a toll on farmers’ health, leading to ailments and respiratory issues stemming from exposure to noxious gases emitted by certain crops. Extensive research endeavours [3-9] have been undertaken to alleviate the burdens faced by farmers. These efforts, however, frequently culminate in singular applications such as automated irrigation systems or electric perimeters for crop protection. A subset of researchers has also delved into probing the prevalence of harmful gases across agricultural fields. This paper proposes an innovative approach to address these challenges through the utilization of Internet of Things (IoT) technology. Objective: The proposed solution canters on a NodeMCU powered intelligent crop field Monitoring-Protection-Alert (MPA) system, which serves as a technical path to revolutionize farming practices. The core objective underpinning the proposed system is the acquisition of real-time insights emanating from the crop field. This critical initiative empowers farmers with timely and accurate data, enabling them to make informed and precise decisions pertaining to their agricultural domain. Methods: By seamlessly integrating various sensors, the system detects the presence of birds, animals, and noxious gases in real-time. Furthermore, it enhances crop productivity by continuously monitoring soil parameters, including temperature and moisture levels, thereby optimizing irrigation processes. For seamless communication, the system is fortified with a GSM module that promptly alerts farmers about potential threats to their crops.

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Reinforcement learning: A novel approach towards drug discovery

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AIP Conference Proceedings 2023年第1期2477卷

作者： Nishath Ansari Amjan Shaik Patluri Harika Boga Varasree K. Purnachand Saikumar Tara Assistant Professor Department of Computer Science and Engineering B V Raju Institute of Technology Narsapur Telangana India Professor Department of Computer Science and Engineering B V Raju Institute of Technology Narsapur Telangana India Assistant Professor Department of Computer Science and Engineering Institute of Aeronautical Engineering Dundigal Hyderabad Telangana India Assistant Professor Department of Information Technology Institute of Aeronautical Engineering Dundigal Hyderabad Telangana India Associate Professor Department of Computer Science and Engineering B V Raju Institute of Technology Narsapur Telangana India Associate Professor Department of Electronics and Communication Engineering CMR Technical campus Hyderabad Telangana India

Reinforcement learning has been giving new strategies to ad-lib in the fields of medication and the drug industry. It is probably the best strategy among all the AI strategies. Reinforcement learning has been compelling to discover strategies that could treat the immense number of deadly infections. In this paper, a detailed new methodology about what Reinforcement Leaning and what is drug revelation and what are various techniques utilized in Reinforcement learning, how Reinforcement learning could be utilized in different utilization of medication plan such as target identification, hit discovery, hit to lead, lead optimization. The consequence of the paper illustrates the summarized data of different approaches for the development and discovery of drugs. A clear illustration of various work done in the field of drug discovery, discuss different pros and cons of Reinforcement learning as well emphasize primary challenges need to handle and how to defeat them in the future.

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Green dream: A deep learning based real-time model for automatic waste segregation from video streams

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AIP Conference Proceedings 2023年第1期2773卷

作者： Prince Puthussery Nikhita Mariam Cherian T. K. Gokul Kiran M. U. Sreeja Abin Oommen Philip Department of Computer Science & Engineering Toc H Institute of Science & Technology Ernakulam Kerala India Assistant Professor Department of Computer Science & Engineering Toc H Institute of Science & Technology Ernakulam Kerala India Assistant Professor Department of CSE-Cyber Security Indian Institute of Information Technology Kottayam Kerala India

In this fast-pacing world one of the substantial problems faced is the drastic increase in waste generation and ensuring efficient and rational management of waste. Recycling tasks reduce waste production, mitigate the environment and improve the whole nation’s prosperity in the future. Inappropriate handling and discarding of useless materials have led to the contamination of groundwater and defiled the land resources. Therefore, it is vital that the methods and processes involved in waste collection and segregation is examined as the present-day disposal system is inefficient, time consuming, cumbersome and not completely viable due to their large amount. The proposed model provides a way of solving the above stated problems by detecting, identifying and segregating the waste materials. The proposed system is an automated waste classifier which uses a deep learning-based object detection model to classify objects into different categories. Using YOLOv5, an object detection algorithm the waste can be identified successfully from the image taken with the help of a camera and classify them into categories. This result is then sent to a segregation unit which categorizes the waste into the designated bin by rotating the conveyor belt using a motor. The proposed model operated at a Mean Average Precision(mAP) of 0.726 and an F1-Score of 0.83. This method of segregation of waste will enable faster ways to recycle the waste and save time and resources.

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Determining the Priority of Factors Influencing the Selection of Information technology for Distance Education

Determining the Priority of Factors Influencing the Selectio...

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International computer sciences and Information Technologies (CSIT)

作者： Oleksandr Tymchenko Orest Khamula Svitlana Vasiuta Olha Sosnovska Solomiya Dorosh Mykhailo Drimaylo Department of Safety Engineering University of Warmia and Mazury Olsztyn Poland Department of Computer science and Information technology Ukrainian Academy of Printing Lviv Ukraine Department of Entrepreneurship and Marketing Ukrainian Academy of Printing Lviv Ukraine Department of Book and Easel Graphics Ukrainian Academy of Printing Lviv Ukraine

ISBN: (纸本)9798350334326

This article considers the task of determining the priority of factors influencing the selection of information technology for distance education. Since this process is one of those ones that are difficult to describe in the form of mathematical formulas, it is suggested to use the graph method and the method of analysis of hierarchies for calculations. These methods have shown themselves to be good in solving this kind of *** the course of the conducted survey among the participants of the educational process, eight factors are singled out, which, according to the correspondents, have a certain weight when selecting the information technology for distance education. Thus, such factors are singled out: communication tools; educational facilities; user data management; usability; adaptation; technical aspects; administration; course management. As the conducted studies show, the factor of usability acquires the highest priority level. The factor of course management, which is on the fourth step of the hierarchy, is at a lower level in terms of importance. According to calculations, the factors of the first hierarchy level turn out to be the least important: communication tools; educational facilities. These research findings are important for those educational institutions that are at a crossroads in their choice and they will help to decide on such information technologies.

关键词： Computational modeling Education Analytic hierarchy process Information technology Usability Task analysis

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Comparative Analysis: Deep vs. Machine Learning for Early DFU Detection in Medical Imaging

Comparative Analysis: Deep vs. Machine Learning for Early DF...

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Intelligent Methods, Systems, and Applications (IMSA)

作者： Ahmed Mostafa Abd El-Aal El-Kady Mohamed Mostafa Heba Hamdy Ali Hussien Farid Ali Moussa Faculty of Computer Science October University for Modern Sciences and Arts Giza Egypt Information Technology Dept. Faculty of Computers & Artificial Intelligence Beni-suef University Beni-suef Egypt Assistant Professor Multimedia Dept. Faculty of Computers & Artificial Intelligence Beni-suef University Beni-suef Egypt

Diabetes Mellitus (DM) is a metabolic disorder characterized by aberrant insulin secretion and function, resulting in elevated blood glucose levels and chronic damage to organs. Diabetic Foot Ulcers (DFU) are a significant complication of DM, frequently leading to lower limb amputation if not effectively managed. DFU imposes a substantial burden on patients, families, and healthcare systems, particularly in developing nations where treatment expenses can be overwhelming. Leveraging machine learning and deep learning techniques allows for accurate identification of foot ulcers in diabetic individuals, facilitating early detection and prompt treatment, consequently mitigating the risk of complications and improving the overall quality of life for DM patients. The study focuses on utilizing deep learning and machine learning models to classify diabetic foot ulcers. The results reveal that the RESNET18 model outperforms the SVM models, achieving an accuracy of 97.9%. The study suggests that deep learning models, notably the RESNET18, are more suitable for identifying diabetic foot ulcers.

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Feature Extraction, Essential Dimension Estimation and Dimension Reduction Method of Colony Images

IAENG International Journal of Computer Science

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IAENG International Journal of computer science 2021年第2期48卷 1-13页

作者： Sun, Wei-Zhong Guo, Fu-Jun Wang, Jie-Sheng Chen, Lin Wei, Dong Du, Xin-Feng A lecturer of School of Computer Science and Software Engineering University of Science and Technology Liaoning Anshan114051 China A postgraduate student of School of Electronic and Information Engineering University of Science and Technology Liaoning Anshan114051 China A professor of School of Electronic and Information Engineering University of Science and Technology Liaoning Anshan114051 China An associate professor of School of Electronic and Information Engineering University of Science and Technology Liaoning Anshan114051 China

A colony is a group of microorganisms produced by the growth and reproduction of a single microorganism species. A method of colony image feature extraction, essential dimension estimation and dimension reduction based on digital image processing technology was proposed. Firstly, based on the HSI image color moments (first, second and third order moments), nine color characteristics of the colony images were extracted. Based on gray-level co-occurrence matrix (GLCM), twenty texture features of colony images were obtained. Then, three essential dimension estimation methods, namely, the correlation dimension estimator, the maximum likelihood estimator and the packing numbers estimator, are used to estimate the dimension of the inner low-dimensional structure of the high-dimensional data of the colony images. The results of different dimension estimators were combined with six different dimension reduction techniques (PCA, LDA, MDS, ISOMAP, SNE and NCA) respectively to form 18 different data dimension reduction methods. Based on the distance criterion function, an optimal dimension reduction method for the high-dimensional feature data of the colony images corresponding to the maximum distance criterion function was obtained. © 2021. All Rights Reserved.

关键词： Feature extraction

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