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14th IEEE Global Engineering education Conference, EDUCON 2023

作者： Urquiza-Fuentes, Jaime Rey Juan Carlos University Móstoles ACTIVATE Teaching Innovation Group Laboratory of Information Technologies in Education - LITE Department of Computer Science & Statistics Madrid Spain

ISBN: (纸本)9798350399431

This work describes an incremental design of a course that implements the flipped classroom methodology. The last phase of this implementation was to include rewards based on self-evaluation tests that would impact the students' grades. This should foster students' engagement with the learning methodology. In this work, a deep analysis of the impact of the reward is performed. The analysis of the use of the out-class materials shows that the students' pace does not correspond to the flipped classroom methodology. The students' behaviour is more focused on achieving rewards rather than preparing in-class activities with out-class materials. It is hypothesized that, the main reasons for these behaviours could be that, on the one hand, since the reward is considered by students as another item of the course grade, they work to get the reward. On the other hand, students do not want to change their study methodology because getting involved in the flipped methodology requires from them a significant effort. © 2023 IEEE.

关键词： Students

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Influenza Diagnosis Deep Learning: Machine Learning Approach for Pharyngeal Image Infection

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EAI Endorsed Transactions on Pervasive Health and Technology 2024年 10卷 1-8页

作者： Chaudhari, Chetan Fegade, Sapana Gantayat, Sasanko Sekhar Jugnu, Kumari Sawan, Vikash Department of Computer Science & Engineering G. H. Raisoni Institute of Engineering & Business Management Shirsoli Road Jalgaon MS Jalgaon Mohadi425002 India Department of Computer Engineering SSBT's College of Engineering & Technology Bambhori Jalgaon Tal-Dharangaon MS Jalgaon Bambhori425 001 India Department of CSE Koneru Lakshmaiah Education Foundation AP Vaddeswaram India Bihar Patna India Monad University Uttar Pradesh Hapur India

INTRODUCTION: Annual influenza epidemics and rare pandemics represent a significant global health risk. Since the upper respiratory tract is the primary target of influenza, a diagnosis of influenza illness might be made using deep learning applied to pictures of the pharynx. Using pharyngeal imaging data and clinical information, the researcher created a deep-learning model for influenza diagnosis. People who sought medical attention for flu-like symptoms were the subjects included. METHODOLOGY: The study created a diagnostic and predicting Artificial Intelligence (AI) method using deep learning techniques to forecast clinical data and pharyngeal pictures for PCR confirmation of influenza. The accuracy of the AI method as a diagnostic tool was measured during the validation process. The extra research evaluated the AI model's diagnosis accuracy to that of three human doctors and explained the methodology using high-impact heat maps. In the training stage, a cohort of 8,000 patients was recruited from 70 hospitals. Subsequently, a subset of 700 patients, including 300 individuals with PCR-confirmed influenza, was selected from 15 hospitals during the validation stage. RESULTS: The AI model exhibited an operating receiver curve with an area of 1.01, surpassing the performance of three doctors by achieving a sensitivity of 80% and a specificity of 80%. The significance of heat maps lies in their ability to provide valuable insights. In AI models, particular attention is often directed towards analyzing follicles on the posterior pharynx wall. Researchers introduced a novel artificial intelligence model that can assist medical professionals in swiftly diagnosing influenza based on pharyngeal images. © 2024 C. Chaudhari et al.

关键词： Diagnosis

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Novel Applications of Cloud Computing and Deep Learning Techniques for Data Analysis 15

Novel Applications of Cloud Computing and Deep Learning Tech...

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15th International Conference on Computing Communication and Networking technologies, ICCCNT 2024

作者： Rajan, V.Aravinda Tiwari, Devesh Parveen, H. Summia Verma, Aditya Shukla, Rishi Prakash Pawar, Vikas V Kalasalingam Academy of Research and Education Krishnankoil Department of Computer Science and Engineering Tamil Nadu Srivilliputhur626126 India Graphic Era Hill University Computer Science and Engineering Uttarakhand Dehradun248002 India Sri Eshwar College of Engineering Department of Computer Science Engineering Tamil Nadu Coimbatore641202 India Graphic Era Deemed to be University Computer Science and Engineering Uttarakhand Dehradun India Jaipuriya Institute of Management Jaipur India Dr. D Y Patil Vidyapeeth Centre for Online Learning Pune India

ISBN: (纸本)9798350370249

In recent years, cloud computing and deep getting to know strategies have emerged as effective gear for information analysis. This combination permits for the efficient processing of large amounts of statistics, permitting researchers to gain precious insights and make informed selections. This technical abstract will talk the unconventional packages of cloud computing and deep learning strategies for statistics evaluation and how they've revolutionized the sphere. Cloud computing has made it feasible to save and get admission to big amounts of statistics from any region, eliminating the need for costly and cumbersome hardware. This has opened up new possibilities for data evaluation as researchers can now access and examine information from a couple of resources: social media, medical studies, and monetary statistics. Deep studying techniques, then again, have proven terrific fulfillment in processing complicated and unstructured statistics. Via mimicking the shape and functions of the human brain, deep mastering fashions can identify styles and traits in information that traditional methods may additionally leave out. © 2024 IEEE.

关键词： Computing Hardware Information Mimicking Statistics

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Optimizing Patient Flow and Resource Allocation in Hospitals using AI 1

Optimizing Patient Flow and Resource Allocation in Hospitals...

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1st International Conference on Artificial Intelligence for Innovations in Healthcare Industries, ICAIIHI 2023

作者： Rathore, Yogesh Sinha, Upasana Haladkar, Jaysing Pandurang Mate, Nilesh R. Bhosale, Supriya Ashok Chobe, Santoshkumar Vaman Shri Shankaracharya Institute of Professional Management and Technology Raipur Dept of Computer Science & Engineering Chhattisgarh India Dept of Computer Science & Engineering India Sant Gajanan Maharaj College of Engineering Department of Electronics and Telecommunication Engineering M.S. Mahagaon416503 India Centre for Distance and Online Education Pune India Sandip University Department of Computer Science & Engineering Nashik India Department of Computer Engineering Pune Ravet India

ISBN: (纸本)9798350330915

The present study investigates the incorporation of artificial intelligence (AI) into healthcare administration, with a specific focus on optimizing patient flow as well as allocating resources in various hospital environments. Using a descriptive research methodology with secondary data gathering, the study adopts an interpretivist philosophy and a deductive technique. The findings show that applying AI-driven techniques significantly improves patient wait times, the efficiency of resource utilization, and patient outcomes. The contrast between AI-enhanced practices and traditional procedures demonstrates how much better they are at optimizing patient flow. On the other hand, implementation-critical elements include initial investment expenses and ethical considerations. Investing in AI infrastructure, resolving algorithmic biases, including protecting data privacy are among the recommendations. Future research ought to concentrate on the scalability, long-term effects, and enhancements to AI systems for objective forecasts © 2023 IEEE.

关键词： artificial intelligence healthcare management interpretivism patient flow optimization resource allocation

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An enhanced Active Reinforcement Learning for Autonomous Robotics in Industrial automation 2

An enhanced Active Reinforcement Learning for Autonomous Rob...

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2nd IEEE International Conference on Industrial Electronics: Developments and Applications, ICIDeA 2023

作者： Rajan, V. Aravinda Marimuthu, T. Bhardwaj, Rajat Shukla, Rishi Prakash Kalasalingam Academy of Research and Education Department of Computer Science and Engineering Tamil Nadu Krishnankoil Virudhunagar626 126 India Rv University School of Computer Science and Engineering Bengaluru560059 India Ait Management Chandigarh University Punjab Mohali India

ISBN: (纸本)9798350381979

An enhanced active reinforcement learning technique has been proposed to enable autonomous robots to operate and execute tasks in industrial automation. This approach combine hierarchical reinforcement learning and Bayesian optimization, to acquire knowledge from complex real-world environments and acquire optimal policies which can enable autonomous robots to perform collaborative tasks efficiently. The main advantage of this enhanced active reinforcement learning approach is the capability of the autonomous robot to autonomously adapt its movements and decision-making strategies when new tasks are required. It allows for the robot to explore its environment and learn how to complete tasks optimally while reducing the burden of manual intervention. Moreover, the proposed approach can generalize its knowledge to establish rewarding collaborative behaviors between robots and humans, thus allowing for collaborative human-robot interactions. This will be beneficial in performing industrial automation with robot cooperative tasks and optimize the efficiency of the industrial automation system.. © 2023 IEEE.

关键词： Decision making

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A Hybrid Bio-inspired Fuzzy Feature Selection Approach for Opinion Mining of Learner Comments

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SN computer science 2024年第1期5卷 135页

作者： Jatain, Divya Niranjanamurthy, M. Dayananda, P. Department of Computer Science and Engineering Maharaja Surajmal Institute of Technology New Delhi India Department of AI and ML BMS Institute of Technology and Management Bangalore India Department of Information Technology Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Manipal India

With more and more teaching learning activities being shifted to online mode, the education system has seen a drastic paradigm shift in the recent times. Learner opinion has emerged as an important metric for gaining valuable insights about teaching–learning process, student satisfaction, course popularity, etc. Traditional methods for opinion mining of learner feedback are tedious and require manual intervention. The author, in this work has proposed a hybrid bio-inspired metaheuristic feature selection approach for opinion mining of learner comments regarding a course. Experimental work is conducted over a real-world education dataset comprising of 110 K learner comments (referred to as educational Dataset now onwards) collected from Coursera and learner data from academic institution MSIT. Based on the experimental results over the collected dataset, the proposed model achieves an accuracy of 92.24%. Further, for comparative analysis, results of the proposed model are compared with the ENN models for different embeddings, viz., Word2Vec, tf-idf and domain-specific embedding for the SemEval-14 Task 4. The hybrid bio-inspired metaheuristic model outperforms the pre-existing models for the standard dataset too. © 2023, The Author(s).

关键词： Bat algorithm Bio-inspired metaheuristic optimisation Data science applications in education Earthworm algorithm Fuzzy sets Modified inverse class frequency Opinion mining

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Exploring Local Governance and Sustainability Challenges in Community Reserves: A Data science Perspective with Global Insights 3

Exploring Local Governance and Sustainability Challenges in ...

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3rd Odisha International Conference on Electrical Power Engineering, Communication and Computing Technology, ODICON 2024

作者： Sachin Pavithran, A.P. Banerjee, Anurima Pradeep Ghantasala, G.S. Rajesh Sharma, R. Ananthanagu, U. Natarajan, Suresh Kumar Vidyullatha, Pellakuri Department of Humanities and Social Sciences Ramanagara India Department of Humanities and Social Sciences India Alliance College of Engineering and Design Alliance University Department of Computer Science and Engineering Bengaluru India Alliance University Center of Excellence in Immersive Technologies Bengaluru India Ramanagara India Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Andhra Pradesh Guntur Vaddeswaram India

ISBN: (纸本)9798350354379

As one of the furthermost noticeable industries, tourism strives for to magnify the situation in numerous forms, effectually addressing the dissimilar matters and concerns into the world nowadays. Frequently, these issues disturb the ecologically momentous areas and the local communities on a higher note. It's the need of the hour to check the forecasts of sustainable and community-based coastal tourism, whichever targets to promote the area's tourism potential over vigorous involvement by the local community although preservative the aboriginal ecological balance. Kerala, a classic tourism destination in India, provides significant opportunities for livelihood options to the local community who depend on natural resources. The contemporaneous research paper was centered on a case study of Kadalundi- Vallikkunnu Community Reserve in Kerala, the local authority and several influences it could need onto the local community then the recommendations whichever could be measured to discourse a interrelated issues. A vast portion of the respondents, having tourism activities as their primary source of income, have identified the problem of environmental degradation as prevalent in the area. In contrast, the rest are unaware of the harmful effects happening to the natural ecosystem, primarily because of waste disposal and water pollution. The paper is an endeavor to shed graceful on the local governance system of community reserve and socio-economic aspects of the local communities on the development of tourism initiatives in Kerala. © 2024 IEEE.

关键词： Community Based Coastal Tourism Community Reserve Environmental Degradation Rural Tourism Sustainability

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Using the Cooperative Nodes for Diminishing the Packet Drops in Mobile WSN 3

Using the Cooperative Nodes for Diminishing the Packet Drops...

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3rd International Conference on Artificial Intelligence and Smart Energy, ICAIS 2023

作者： Elangovan, Vetrimani Dhawaleswar Rao, C.H. Arul Prasath, A. Padmapriya, G. Presidency University Department of Computer Science and Engineering Karnataka Bangalore India Centurion University of Technology and Management Department of Computer Science and Engineering Odisha Gajapati Sitapur India Vel Tech Rangarajan Dr. Sagunthala R and D Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Srm Institute of Science and Technology School of Computing Department of Computing Technologies Tamil Nadu Chennai India

ISBN: (纸本)9781665462167

In Mobile Wireless Sensor Network (MWSN), sensor nodes build an active wireless network lacking existing structure. The MWSN contains several mobile sensor nodes that move randomly in the topology. In MWSN, Diminishing the packet drops is a significant challenge. Several approaches are introduced to handle packet losses. However, these approaches can't be able to manage the packet losses efficiently. To solve this issue, the Cooperative nodes for diminishing the packet drops in MWSN are used. In this approach, the cooperative nodes decrease packet loss during data transmission. In this approach, the suitable nodes observe every data transmission inside the communication range nodes. If suppose, any packet losses during data transmission, these cooperative nodes are captured the lost packets and then forward them to the near neighbor nodes. Finally, the data reach the destination via a regular route. As a result, avoids packet losses in the MWSN. Simulation results show that the proposed method performs better than the existing mechanisms. © 2023 IEEE.

关键词： Packet loss

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Secure RFID-Assisted Authentication Protocol for Vehicular Cloud Computing Environment

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IEEE Transactions on Intelligent Transportation Systems 2024年第9期25卷 12528-12537页

作者： Saleem, Muhammad Asad Li, Xiong Mahmood, Khalid Tariq, Tayyaba Alenazi, Mohammed J. F. Das, Ashok Kumar University of Electronic Science and Technology of China School of Computer Science and Engineering Chengdu611731 China Institute for Cyber Security School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu611731 China Sichuan University Key Laboratory of Data Protection and Intelligent Management Ministry of Education Chengdu610065 China Shenzhen Institute for Advanced Study University of Electronic Science and Technology of China Shenzhen518000 China National Yunlin University of Science and Technology Graduate School of Intelligent Data Science Yunlin Douliu64002 Taiwan National Yunlin University of Science and Technology Graduate School of Engineering Science and Technology Yunlin Douliu64002 Taiwan King Saud University College of Computer and Information Sciences Department of Computer Engineering Riyadh11451 Saudi Arabia International Institute of Information Technology Center for Security Theory and Algorithmic Research Hyderabad500032 India

Vehicular network technology has made substantial advancements in recent years in the field of Intelligent Transportation Systems. Vehicular Cloud Computing (VCC) has emerged as a novel paradigm with a substantial increase in data exchange within Vehicular ad-hoc networks (VANETs). VCC integrates cloud computing, vehicular networking, and Internet of Things (IoT) technologies. It enables Infrastructure-to-Vehicle (I2V), Vehicle-to-Vehicle (V2V), and Vehicle-to-Device (V2D) communication. VCC optimizes vehicle, cloud infrastructure, and IoT resources while addressing significant communication security and vehicle-user privacy challenges. To address these issues, we developed an RFID-based authentication protocol for VCC based on a Henon map using a hash function. In addition, we also incorporated a Physical Unclonable Function (PUF) to resist physical tampering attacks. We validate the protocol's security formally and informally. The formal security analysis is conducted through a widely used RoR model. We use the Scyther simulation tool to verify the proposed protocol's security against various attacks. Moreover, we compare the performance of our protocol with similar existing protocols across important performance parameters such as communication and computation overheads and security attributes. The proposed protocol yields substantial improvements, demonstrating a 40.74% reduction in computation overhead and a 13.03% decrease in communication overhead as compared to related protocols, delivering both enhanced performance and resource efficiency. The analysis demonstrates its capacity to support secure communication in the VCC environment and satisfy desirable security attributes. © 2024 IEEE.

关键词： Authentication

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DeepBone: using Convolutional Neural Networks to Improve Diagnostic Precision in Bone X-Ray Analysis 1

DeepBone: using Convolutional Neural Networks to Improve Dia...

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1st International Conference on Computational Intelligence for Security, Communication and Sustainable Development, CISCSD 2024

作者： Vidyullatha, Pellakuri Pradeep Ghantasala, G.S. Bhoite, Amol Vatsa, Madhuranjan Kallam, Suresh Rajendran, Vijayalaxmi Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Andhra Pradesh Vaddeswaram Guntur India Alliance University Department of Computer Science and Engineering Bengaluru India Krishna Vishwa Vidyapeeth Deemed to Be University Krishna Institute of Medical Sciences Department of Radiology Maharashtra Karad India Akhildev Ipr and Research Services UP Greater Noida India School of Computer Science and Engineering Ramanagara India Christ University School of Business and Management Delhi-NCR India

ISBN: (纸本)9798350365405

Convolutional neural networks (CNNs) as an innovative means of enhancing and automating the examination of bone X-rays. By eradicating the strenuous and subjective nature of modern manual assessments, which can cause faults and delay the investigative process, our clarification resolves these disadvantages. Our method influences a modified CNN architecture and a curated dataset of both standard cases and many bone disorders to capture complicated patterns in X-ray imaging. DeepBone, after wide training and testing, displays exceptional sensitivity and specificity and exceeds modern methods in terms of effectiveness and accurateness. The testing results show that DeepBone can considerably reduce the volume of work that medicinal staff must accomplish though accelerating the dispensation of bone X-rays and preserving analytical accuracy. Our outcomes also clarify the interpretability for deep learning models for imaging the musculoskeletal system. DeepBone is a substantial progression toward more dependable and accessible analytic methods in musculoskeletal radiography, refining patient outcomes and medical workflows. Using deep learning, we can advance the efficiency and exactness of bone X-ray examination, which will eventually advance patient care. © 2024 IEEE.

关键词： Musculoskeletal system

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