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A Flipped Design of Learning Resources for a Course on Algorithms and data Structures

A Flipped Design of Learning Resources for a Course on Algor...

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9th international conference on Interactive Collaborative and Blended Learning, ICBL 2020

作者： Maiorana, Francesco University of Urbino Urbino Italia61029 Italy Kansas State University ManhattanKS United States

ISBN: (纸本)9783030672089

There is a worldwide effort to introduce Computer science (CS) to students from primary school level through higher education, and this effort has been sustained by the whole education community from teachers to researchers. The paper describes the design decisions and development processes of accessible and inclusive learning resources (LR) suitable for infusing computational thinking (CT) and computing into the STE(A)M, i.e. science, Technology, Engineering, Mathematics, and All other disciplines. The developed curriculum supports quality education for all. The developed learning resources have been used in an online course on algorithms and data structures for undergraduate students not majoring in Computer science. Content, Pedagogies, and technologies will be discussed along with a comparison and a contrast of different learning trajectories. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： data structures

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Detection of EDoS attacks in SDN-based Cloud Model using Deep Learning based SDPN Technique

Detection of EDoS attacks in SDN-based Cloud Model using Dee...

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Smart technologies in computing, Electrical and Electronics (ICSTCEE), international conference on

作者： Suguna M Mohan M Srikanth J M Suresh P G Banupriya Logeshwari Dhavamani Department of Computer Science and Engineering SNS College of Engineering Coimbatore India Department of Computer Science and Engineering KPR Institute of Engineering and Technology Coimbatore India Department of Computer Science and Technology SNS College of Engineering Coimbatore India Department of Computer science and Engineering School of Computing Science QIS College of Engineering and Technology Ongole Andhra Pradhesh India Department of Computer Science and Engineering Karpagam Institute of Technology Coimbatore India Department of Information Technology St. Joseph's College of Engineering Chennai India

Offering IT services to businesses and consumers via the cloud is increasingly seen as the most cost-effective model. But it is vulnerable to emerging flaws. In particular, a recently identified form of attack known as an economic-denial-of-sustainability (EDoS) takes advantage of the pay-per-use model to gradually increase the utilisation of the cloud's resources, forcing the customer to fork over further money. Therefore, in this learning, we present a novel method for detection and mitigating EDoS assaults in the SDN-based cloud computing setting utilising a Stack Deep Polynomial Network (SDPN). First, the SDPN learns precise representations of multivariate time sequence in order to imitate the typical patterns. Then, the reconstructed input data is compared to the original. Finally, the probabilities derived from the reconstruction process may serve as both a tool for detecting outliers and a source of insight into what those outliers could mean. Existing systems often utilise a hard threshold to examine the anomalies, which leads to growing mistake rates; in contrast, the suggested scheme introduces a threshold value utilising SDPN to lower error rates. When likened to other solutions and our past work, the findings of this extensive analysis indicate remarkable performance.

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Health Review and Analysis Using data science 6th

Health Review and Analysis Using Data Science

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6th international conference on Emerging Research in computing, Information, Communication and Applications, ERCICA 2020

作者： Dutta, Debashish Das, Shivarpan Nath, Aritra Kaushik, Abhyuday Shobha, P. Department of Computer Science and Engineering Nitte Meenakshi Institute of Technology Bengaluru Karnataka India

ISBN: (纸本)9789811613371

Health care has been a major concern for everyone right from the inception of mankind. There is no doubt that medical science has done remarkable headway in this arena too. Here, the detection of disease a person is suffering from is a key aspect and hence, in this project we proposed a model which primarily focuses on having easy diagnosis and prediction of the disease. Moreover, the primary objective of this project is to provide remote diagnosis to the patients. In this suggested system the user can provide the input either by speech or entering directly into the UI. The proposed model once detecting the disease also displays the description of the same and a more info link for further elucidation. Along with it the user is also provided with important health tips, a balanced diet plan and required exercises which will help the user to make the required changes in their diet and daily routine which would lead to a potential healthy lifestyle. Apart from this, as per the current situation of the ongoing pandemic we also added few other features other than the above mentioned like a COVID-19 tracker which helps the user to stay updated with the state wise count which gives us the total confirmed,active, recovered and deceased cases according to the Ministry of Health and Family Welfare department and it is purely dynamic in nature. Also, we are providing the potential causes of the disease and the preventive measures to be taken by the user as per the government guidelines. By this, we are trying to achieve better health care in a technological aspect. Using the latest technologies will make it much efficient to know about various symptoms and predict the cause at an early stage which will help in taking necessary steps to minimize the damage. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Support vector machines

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data Analysis on Dental Patients with Medical Co-Morbidities Using Machine Learning Algorithm

Data Analysis on Dental Patients with Medical Co-Morbidities...

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international conference on computing and Networking Technology (ICCNT)

作者： Umme Saima Nitish Krishna Das A. B. M. Aowlad Hossain Department of Biomedical Engineering Khulna University of Engineering & Technology Khulna Bangladesh Department of Oral & Maxillofacial Surgery Shaheed Suhrawardy Medical College Dhaka Bangladesh Department of ECE Khulna University of Engineering & Technology Khulna Bangladesh

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

From the eve of the advancement of medical science, oral and dental health care has gained importance with severe medical health issues and medical disabilities. The dentist must be careful about any medically compromised patient providing dental or oral treatment as that should be safe and effective. In this paper, we have explained dental patients with medical co-morbidities for a real dataset to study the pervasiveness of medical co-morbidities in patients who need dental treatments. We have collected around 3600 instances with multiple comorbidities of patients visited for dental treatment and regular check-ups from January 2020 to February 2022 in three different hospitals in Bangladesh. From 3600 patients, more than $30 \%$ patients aged above 30 years had co-morbidities in this study. The absolute view of this study is to preprocess the data and then show correlations among them. Our target is to improve the performance using machine learning classifiers and create a training data set. We have used artificial neural network (ANN) and random forest (RF) classifiers and got accuracies $\mathbf{9 8 . 6 3 \%}$ for ANN and $\mathbf{9 7 . 4 5 \%}$ for RF. Co-morbidity is a co-existing medical condition distinct from a focal state, making the patient look for medical services. Unpleasant health condition is related to complex clinical management and that may increase healthcare costs. This paper also described the relationship between dental patients with medical co-morbidities like hypertension (HTN), Diabetes, Asthma, hypothyroidism, cardiac complications, renal diseases, liver disease, and some oro-pharyngeal complications related to dental diseases, etc.

关键词： Hypertension Surveys Accuracy Training data Artificial neural networks Dentistry Diabetes Medical diagnostic imaging Diseases Classification tree analysis

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Analytical Design of the DIS Architecture: The Hybrid Model 1st

Analytical Design of the DIS Architecture: The Hybrid Model

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1st FICR international conference on Rising Threats in Expert Applications and Solutions, FICR-TEAS 2020

作者： Prakash, B.R. Hanumanthappa, M. Dattasmita, H.V. Kavitha, Vasantha Department of Computer Government First Grade College TipturKarnataka India Department of Computer Science & Applications Bangalore University BangaloreKarnataka India PhD Scholar Siddhartha Academy of Higher Education TumkuruKarnataka India Maharani Lakshmi Ammanni College for Women BangaloreKarnataka India

ISBN: (纸本)9789811560132

In the last few decades, and due to emergence of internet appliance, there is a strategical increase in the usage of data which had a high impact on the storage and mining technologies. It is also observed that the scientific/research field produces the zig-zag structure of data, viz., structured, semi-structured, and unstructured data. Comparably, processing of such data is relatively increased due to rugged requirements. There are sustainable technologies to address the challenges and to expedite scalable services via effective physical infrastructure (in terms of mining), smart networking solutions, and useful software approaches. Indeed, the cloud computing aims at data-intensive computing, by facilitating scalable processing of huge data. But still, the problem remains unaddressed with reference to huge data, and conversely, the data is growing exponentially faster. At this juncture, the recommendable algorithm is a well-known model, that is, MapReduce, to compress the huge and voluminous data. Conceptualization of any problem with the current model is less fault-tolerant and reliable, which may be surmounted by Hadoop architecture. On the contrary case, Hadoop is fault-tolerant and has the high throughput which is recommendable for applications having huge volume of data sets, thus file system requiring the streaming access. The paper examines and unravels what efficient architectural/design changes are necessary to bring the benefits of the Everest model, HBase algorithm, and the existing MR algorithms. © 2021, Springer Nature Singapore Pte Ltd.

关键词： MapReduce

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Recent Breakthroughs in Cognitive Informatics and Cognitive computing towards Autonomous AI (Plenary Panel Report-I of IEEE ICCI*CC'22)

Recent Breakthroughs in Cognitive Informatics and Cognitive ...

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IEEE international conference on Cognitive Informatics

作者： Yingxu Wang Bernard Widrow Witold Pedrycz Janusz Kacprzyk Sam Kwong Konstantin.N. Plataniotis Ling Guan Petros Spachos Dept. of ESE FIEEE University of Calgary AB Canada Dept. of Electrical Engg FIEEE Stanford Univ. USA Dept of ECE FIEEE Univ. of Alberta AB Canada FIEEE Systems Research Institute Polish Academy of Sciences Poland Dept. of Computer Science FIEEE Hongkong City Univ. Hong Kong Dept. of ECE FIEEE Univ. of Toronto Canada FIEEE Ryerson Multimedia Research Laboratory Ryerson University Toronto Canada Univ. of Guelph Canada

Basic research in Cognitive Informatics (CI) and Cognitive computing (CC) provides fundamental theories of intelligence science for Autonomous AI (AAI) and cognitive systems. The field of CI and CC has led to general AI technologies triggered by the transdisciplinary advances in brain, intelligence, computer, knowledge, cognitive, robotic, and cybernetic sciences for engineering implementations. This paper presents a summary of the plenary panel (Part I) on the “Recent Breakthroughs in Cognitive Informatics and Cognitive computing towards AAI” in the 21st IEEE international ICCI*CC conference (ICCI*CC'22). Strategic CI/CC applications are presented in cognitive systems, AAI, cognitive robots, intelligent vehicles, AI knowledge learning, autonomous intelligence generation, and safety-and-mission-critical systems.

关键词： Knowledge engineering Intelligent vehicles Cognitive informatics Cognitive robotics Cognitive systems Artificial intelligence

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Design and implementation of fitness management website

Design and implementation of fitness management website

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advances in computing, Communication Control and Networking (ICAC3N), international conference on

作者： Ajitesh Sharma Yatin Pandey Department of Computer Science Galgotias University Greater Noida India

Summarizing today’s hectic lifestyle people must be fit and healthy for tension free life. Fitness are often described as a condition that helps us look, feel and do our greatest. As you know, this coronavirus pandemic has changed the lives of everyone., also it impacted our health badly. because of this there's a really big impact on the fitness. So, taking both major points discussed into consideration we are visiting develop a dynamic end-to-end Single Page Application (SPA) supported FITNESS, for the those who are willing to step-up and choose a healthy lifestyle for them by enrolling on this website. there'll be three entities within the system namely EnerGyM Planet i.e. Admin, Member and Trainer. Admin will do administrative tasks like adding, removing and updating information associated with other entities, Member will have features like aiming to choose their trainer from listed trainers available within the gym and so progressing to watch their videos and obtain fitness tips and various information associated with fitness industry. Also, a member will have various attractive features on website like online music for the workout, online payment, also a noteworthy questionnaire having feature with the assistance of AI. technologies used are HTML5, JavaScript, CSS3, ***, Bootstrap-4, React, ***, *** and MySQL

关键词： Industries COVID-19 Planets Pandemics Task analysis Artificial intelligence Videos

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Smart Driver Assistance for Traffic Sign, Pothole, Vehicle Malfunction, and Accident Detection

Smart Driver Assistance for Traffic Sign, Pothole, Vehicle M...

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international conference on advances in ICT for Emerging Regions

作者： Warna Vithanage Hasanka Madushan Tharinda Madushanka Tharushi Lokuliyana Janaka Wijekoon Sanjeevi Chandrasiri Faculty of Computing Sri Lanka Institute of Information Technology Malabe Sri Lanka

Reducing ever-increasing road accidents is a big concern worldwide. Sri Lanka had the highest rate of road fatalities in the past few years, rapidly increasing daily. Among many factors, traffic signs, potholes, and vehicle mechanical malfunctions significantly impact road safety. Most accidents result from a lack of awareness, ignorance, and negligence of drivers. While many high-end vehicles are equipped with technologies such as intelligent road sign recognition systems and air suspension systems, most cars in the market only come with basic driving instruments. Therefore, there is a need for a universal driver assistance system that can be plugged into any vehicle to assist drivers in minimising road casualties. To this end, this study discusses Neural Networks, Machine Learning and IoT technologies to develop an intelligent system that is capable of detecting and analysing road signs, road potholes, vehicles’ internal system malfunctions, and road accidents and notifying drivers in real-time and inform authorities such as hospitals and police stations to be aware of accidents to minimise further casualties. This portable device is based on a Raspberry Pi microprocessor. It uses a web camera, an onboard diagnostic tool (OBD) and an accelerometer to process traffic sign footages, vehicle sensor data and movement data of the vehicle. Yielded results showed that the proposed system was 90% accurate.

关键词： Suspensions (mechanical systems) Road accidents Machine learning algorithms Law enforcement Microprocessors Neural networks Machine learning

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Accuracy-Fairness Tradeoff in Parole Decision Predictions: A Preliminary Analysis

Accuracy-Fairness Tradeoff in Parole Decision Predictions: A...

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IEEE/ACM international Symposium on Big data computing (BDC)

作者： John W. Gardner Furkan Gursoy Ioannis A. Kakadiaris Dept. of Computer Science Computational Biomedicine Lab University of Houston Houston TX USA

Algorithms play an essential and expanding role in public policy decisions, including those in criminal justice. This short paper reports on the first author’s summer research project characterizing the tradeoff between accuracy and fairness in parole decision predictions. The dataset employed in this study contains over 30,000 parole decisions made by the New York State Division of Criminal Justice Services. Each decision contains information on the subject, such as sex, race/ethnicity, and parole decision, as well as predictive features describing the crime committed by the subject and the parole interview held. Logistic regression, decision tree, support vector machine, and random forest models are trained and utilized to analyze parole decision predictions based on the available features. Most models fail to pass standard fairness tests for most fairness metrics. Moreover, while there may be an overall tradeoff between fairness and accuracy, the obtained differences in accuracy are too small to make a well-supported claim. Future research may enhance the preliminary work introduced in this paper by using multiple real-world datasets to investigate the tradeoff between accuracy and fairness.

关键词： Measurement Support vector machines Analytical models Predictive models Prediction algorithms Public policy Decision trees

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Optimal Sign language recognition employing multi-layer CNN

Optimal Sign language recognition employing multi-layer CNN

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advances in computing, Communication Control and Networking (ICAC3N), international conference on

作者： Pranav Pranav Rahul Katarya Dept. of Computer Science Engineering Delhi Technological University Delhi India

We generally convey our ideas, thoughts, and facts through vocal communication. However, not everyone is gifted with the ability to express oneself. verbally. The deaf and mute populations have a difficult time expressing their thoughts and ideas to others; Sign Language (SL) is their most expressive mode of communication, but the majority of the general population is illiterate in SL; as a result, the mute and deaf have difficulty communicating with the rest of the world. A device that can reliably translate SL gestures to voice and vice versa in real-time is required to overcome this communication barrier. The current solutions are not real-time, have poor recognition accuracy, and need static environmental conditions. Some systems might need additional hardware components, such as expensive sensors, which boost the cost. The current methods allowing silent people to communicate their thoughts to others may be divided into two categories: systems based on computer vision and systems based on electrical sensors, each with its own set of benefits and drawbacks. In comparison to previous state-of-the-art approaches, we offer a Convolution neural network (CNN) based SLR system with many layers that gives great accuracy of 99.89%. Overall, we believe the study will serve as road map for future research in the domain of SLR.

关键词： Support vector machines Convolution Sociology Neural networks Gesture recognition Assistive technologies Sensor systems

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