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2nd International Conference on Augmented Intelligence and Sustainable Systems, ICAISS 2023

作者： Rajaram, Gnanajeyaraman Archana Vishveswari, R.S. Deepa, R. Packialatha, A. Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Applied Machine Learning Tamil Nadu Chennai India Varuvan Vadivelan Institute of Technology Department of Computer Science and Engineering Tamil Nadu Dharmapuri India Vels Institute of Science Technology & Advanced Studies Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350325799

Mobile Ad hoc Networks (MANETs) have been broadly functional in a wide variety of scenarios, for example, in disaster recovery, health care, video conferencing, and battlefield transmissions. MANET is an independent and peer-to-peer network which builds a chain of nodes to enable data transmission from sender to receiver. Hop count is measured as the most significant metric in MANET. Though several metrics have been introduced as the replacement for the hop count metric, there is a need for outperformance in mobile scenarios. The node mobility will affect the link stability, and it creates congestion. To solve these issues, a Node Mobility and Encounter Rate metrics (NMER) to enhance the stability of the network. It selects the route based on the node mobility and Encounter Rate (ER) metrics. The congestion ingredient measures the congestion level in the MANET. The objective of this route has the least cost for forward data packets and minimizes congestion. The NMER approach simulation results demonstrate that the NMER approach minimizes the packet loss ratio and reduces the network delay. © 2023 IEEE.

关键词： Mobile ad hoc networks

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Experimental Evaluation of Remote Sensing–Based Climate Change Prediction Using Enhanced Deep Learning Strategy

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Remote Sensing in Earth Systems sciences 2024年第4期7卷 642-656页

作者： Madhavi, Macharapu Kolikipogu, Ramakrishna Prabakar, S. Banerjee, Sudipta Maguluri, Lakshmana Phaneendra Raj, G. Bhupal Balaram, Allam Department of Computer Science and Engineering Velagapudi Ramakrishna Siddhartha Engineering College Andhra Pradesh Vijayawada India Department of Information Technology Chaitanya Bharathi Institute of Technology Hyderabad India School of Commerce and Management Studies Dayananda Sagar University Bangalore India Department of Computer Science and Engineering Symbiosis Institute of Technology Symbiosis International (Deemed University) (SIU) Pune Campus Maharashtra Pune India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation AP Vaddeswaram Guntur India School of Agriculture SR University Warangal India Department of Computer Science and Engineering MLR Institute of Technology Hyderabad India

Climate change is one of the most pressing global challenges of our time, with far-reaching impacts on ecosystems, economies, and human societies. Accurate prediction of climate change patterns is crucial for developing effective mitigation and adaptation strategies. Remote sensing data, with its ability to provide comprehensive and continuous observations of the Earth’s surface, plays a vital role in monitoring and predicting these changes. However, the complexity and high dimensionality of remote sensing data present significant challenges for traditional predictive models. In this study, we present an Enhanced Deep Learning Strategy for climate change prediction using remote sensing data, integrating a Cascaded Inception-LGBM model. The proposed model combines the feature extraction capabilities of the Inception module with the predictive power of the Light Gradient Boosting Machine (LGBM). The methodology was evaluated on various climate variables, including temperature, precipitation, and CO₂ levels, achieving an accuracy of 97.22%. Comparative analysis with state-of-the-art models demonstrated the superior performance of our approach, particularly in terms of RMSE, MAE, and R² metrics. Robustness tests further confirmed the model’s generalization capabilities under different data conditions. This study underscores the potential of advanced deep learning techniques in enhancing climate change prediction accuracy and offers insights into the key drivers of climate variability. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Climate change prediction Deep learning Environmental monitoring Feature extraction Inception module Light Gradient Boosting Machine Model accuracy NDVI Remote sensing Spectral indices

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Diverse Hazy Image Synthesis via Coloring Network

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第7期5卷 3703-3713页

作者： Zhang, Shengdong Zhang, Xiaoqin Wan, Shaohua Ren, Wenqi Zhao, Liping Zhao, Li Shen, Linlin Shaoxing University Department of Computer Science and Engineering Shaoxing312000 China Shenzhen United Aircraft Technology Company Ltd. Guangdong Shenzhen518129 China Wenzhou University College of Computer and Artificial Intelligence Wenzhou325035 China University of Electronic Science and Technology of China Shenzhen Institute for Advanced Study Shenzhen518110 China Sun Yat-sen University School of Cyber Science and Technology Shenzhen518110 China Shenzhen University College of Computer Science and Software Engineering Shenzhen518060 China

Convolutional neural network (CNN)-based dehazing methods have achieved great success in single image dehazing. However, the absence of real-world haze image datasets hinders the deep development of single image dehazing. To address this issue, we propose a diverse hazy image synthesis method based on generative adversarial network (GAN) and matting. Specially, we train a GAN-based model that can transform a gray image into a hazy image. To boost the diversity of hazy images, we propose to simulate hazy images via image matting, which can fuse a real haze image with another image containing diverse objects. To evaluate the performance of dehazing methods, we propose two novel metrics: part-based peak signal-to-noise ratio (PSNR) and structure similarity index measure (SSIM). Extensive experiments are conducted to show the effectiveness of the proposed model, dataset, and criteria. © 2020 IEEE.

关键词： Three dimensional displays

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Inferring Electrocardiography from Optical Sensing Using Lightweight Neural Network

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第7期5卷 3535-3550页

作者： Li, Yuenan Tian, Xin Zhu, Qiang Wu, Min University of Maryland College ParkMD20742 United States Tianjin University School of Electrical and Information Engineering Tianjin300072 China University of Maryland Department of Electrical and Computer Engineering College ParkMD20742 United States Meta Inc. Menlo ParkCA94025 United States University of Maryland Department of Electrical and Computer Engineering Institute for Advanced Computer Studies College ParkMD20742 United States

This article presents a computational solution that enables continuous cardiac monitoring through cross-modality inference of electrocardiogram (ECG). While some smartwatches now allow users to obtain a 30-s ECG test by tapping a built-in bio-sensor, these short-term ECG tests often miss intermittent and asymptomatic abnormalities of cardiac functions. It is also infeasible to expect persistently active user participation for long-term continuous cardiac monitoring in order to capture these and other types of cardiac abnormalities. To alleviate the need for continuous user attention and active participation, we design a lightweight neural network that infers ECG from the photoplethysmogram (PPG) signal sensed at the skin surface by a wearable optical sensor. We also develop a diagnosis-oriented training strategy to enable the neural network to capture the pathological features of ECG, aiming to increase the utility of reconstructed ECG signals for screening cardiovascular diseases (CVDs). We also leverage model interpretation to obtain insights from data-driven models, for example, to reveal some associations between CVDs and ECG/PPG and to demonstrate how the neural network copes with motion artifacts in the ambulatory application. The experimental results on three datasets demonstrate the feasibility of inferring ECG from PPG, achieving a high fidelity of ECG reconstruction with only about 40 000 parameters. © 2020 IEEE.

关键词： Electrocardiograms

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Exploring the Relationship between Participant Role and Collaborative Quality in Online Collaborative Discussions

International Journal of Emerging Technologies in Learning

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International Journal of Emerging Technologies in Learning 2023年第11期18卷 273-289页

作者： Mi, Huichao Zhang, Qiaorong Zheng, Yafeng College of Computer and Information Engineering Henan University of Economics and Law Zhengzhou China A Center for Educational Science and Technology Institute of Advanced Studies in Humanities and Social Sciences Beijing Normal University Guangdong Province Zhuhai China

The exploration of the role concept has become an important perspective for analyzing and promoting computer-supported collaborative learning (CSCL). Understanding the relationships between individual participation roles and collaborative performance is of great significance to the research of collaborative learning theory, pedagogy and technology. However, few empirical studies investigated the individual participation roles in collaborative discussions and the impact of participation role configuration on group performance. Based on the interactive content of learners in collaborative discussions, this research uses machine learning methods to automatically identify learners’ participating roles. Through cluster analysis, five different roles are identified: leader, problem solver, coordinator, marginal learners and learners with difficulties. Furthermore, this research explores the relationships between individual participation roles and group collaboration quality. The results show that groups with different collaboration performances have different role compositions, and the roles of leader, problem solver and coordinator have significant positive effects on collaboration performance. Learners with difficulties have a negative impact on collaboration performance. Combining the research results with the discussion content of the learners, this research conducted an in-depth discussion and analysis of the characteristics of each role, and proposed implications for teaching guidance and researchers. © 2023, International Journal of Emerging Technologies in Learning. All Rights Reserved.

关键词： Cluster analysis

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Exploring The Socio-Cognitive Determinants of Antibiotic Compliance: The Mediating Role of Antimicrobial Resistance Awareness

Exploring The Socio-Cognitive Determinants of Antibiotic Com...

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International Conference on Inventive Computation Technologies (ICICT)

作者： Fathima Rumaiza S. Kamalakkannan Department of Computer Science School of Computing Sciences Vels Institute of Science Technology and Advanced Studies Pallavarm Chennai India Department of Computer Applications School of Computing Sciences Vels Institute of Science Technology and Advanced Studies Pallavaram Chennai India

ISBN: (数字)9798331512248

ISBN: (纸本)9798331512255

Antimicrobial resistance (AMR) is a meaningful and growing public health concern that is largely driven by improper antibiotic use. An important factor contributing to AMR is the failure to complete prescribed antibiotic courses, which allows resistant bacteria to survive as well as spread. This study examines the effect of awareness of AMR on the likelihood that people will adhere to their prescribed antibiotic regimens. Using a chi-square test for independence, the analysis identifies a statistically meaningful association between AMR awareness and the completion of antibiotic courses. Results indicate that people who are aware of AMR and its risks are more likely to complete their prescribed courses than those with lower awareness levels. The study explores several factors influencing adherence, including personal health beliefs, access to adequate medical advice and common misconceptions about antibiotic use, along with this key finding. Many people discontinue antibiotics after symptoms subside, completely unaware of the meaningful risks linked with incomplete treatment. Effectively dealing with these issues is important for considerably reducing the spread of AMR. The study suggests multiple public health interventions to improve antibiotic compliance, which include educational campaigns, increased patient counseling and stricter prescription guidelines. These strategies can considerably help curb the rising threat of AMR by importantly increasing awareness and promoting responsible antibiotic use.

关键词： Employee welfare Antibiotics Bacteria Public healthcare Immune system Guidelines

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Impact of Social Media Influencer on Consumer Decision Making 12

Impact of Social Media Influencer on Consumer Decision Makin...

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12th International Conference on System Modeling and Advancement in Research Trends, SMART 2023

作者： Jha, Ashish Kr. Bajpai, V.N. Kumar, Satish Upadhyay, Sunil Institute of Technology & Science Department of Management Studies Ghaziabad India Banasthali Vidyapith Department of Computer Science Rajasthan Banasthali304022 India

ISBN: (纸本)9798350369861

Social Media Influencer has become the name of the game in the world of advertisement today. Use of famous Social Media influencers is the utilization of influencers to enhance brand image and boost sales has become a widely adopted strategy by almost every company. India, in particular, has a strong tradition of revering influencers from the world of cinema. However, this practice has sparked a controversial debate on whether it genuinely aids in the brand-building process and subsequently leads to increased sales, or if it is merely a superficial tactic that adds costs ultimately borne by consumers. It is noteworthy that the presence of a renowned influencer can generate considerable buzz, albeit at times unnecessarily, thereby addressing the issue of excessive communication that plagues today's competitive business landscape. Consequently, it is imperative to delve into whether advertisements featuring influencers have a positive impact on people and whether they genuinely contribute to added value for both consumers and the company as a whole. This study employs a descriptive research approach and a non-probability sampling design, specifically the convenience method, for data collection. Data were collected from 520 respondents covering the areas of NCR (Near Capital Region) including Delhi, Ghaziabad, Noida and Greater Noida. © 2023 IEEE.

关键词： Image enhancement

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Integrating All-optical Switching and Entangled Photon Source Placement for Entanglement Routing 31

Integrating All-optical Switching and Entangled Photon Sourc...

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31st IEEE/ACM International Symposium on Quality of Service, IWQoS 2023

作者： Zhu, Qiucheng Zhao, Yangming Xu, Hongli Huang, Liusheng Qiao, Chunming School of Computer Science and Technology University of Science and Technology of China China Suzhou Institute for Advanced Research University of Science and Technology of China China University at Buffalo Department of Computer Science and Engineering United States

ISBN: (纸本)9798350399738

Entanglement routing plays a vital role in quantum networks. Previous works on entanglement routing did not note that all-optical switching capacity can be used to improve the network throughput or ignored that the placement of Entangled Photon Sources (EPS), another type of precious (and costly) quantum source, which also limits the network throughput. In this paper, we propose OptEPS to jointly optimize all-optical switching and EPS placement in entanglement routing to maximize the quantum network throughput. The main challenge of OptEPS lies in two folds: i). an entanglement may fail to be created;ii). the joint optimization problem suffers from a large time complexity. To overcome these challenges, we first formulate the joint optimization problem as a link-path based model and prune out some candidate paths in order to reduce the time complexity. Then, efficient algorithms are proposed to derive a near-optimal solution in a timely manner. Extensive simulations show that compared with the solutions ignoring EPS placement or without introducing all-optical switching, OptEPS will increase the network throughput by 1557% and 109%, respectively. © 2023 IEEE.

关键词： Photons

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Provably Efficient Multi-Cancer Image Segmentation Based on Multi-Class Fuzzy Entropy

Informatica (Slovenia)

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Informatica (Slovenia) 2023年第8期47卷 77-88页

作者： Jasim, Hend Muslim Ghrabat, Mudhafar Jalil Jassim Abdulrahman, Luqman Qader Nyangaresi, Vincent Omollo Ma, Junchao Abduljabbar, Zaid Ameen Abduljaleel, Iman Qays Department of Computer Science College of Education for Pure Sciences University of Basrah Basrah61004 Iraq Iraqi Commission for Computers and Informatics The Informatics Institute for Postgraduate Studies Baghdad Iraq Computer Science Department Al-Turath University College Baghdad10013 Iraq College of Health Science Hawler Medical University Erbil Iraq Jaramogi Oginga Odinga University of Science and Technology Bondo40601 Kenya College of Big Data and Internet Shenzhen Technology University Shenzhen518118 China Technical Computer Engineering Department AL-Kunooze University College Basrah6100 Iraq Huazhong University of Science and Technology Shenzhen Institute Shenzhen518000 China Department of Computer Science College of Computer Science and Information Technology University of Basrah Basrah61004 Iraq

One of the segmentation techniques with the greatest degree of success used in numerous recent applications is multi-level thresholding. The selection of appropriate threshold values presents difficulties for traditional methods, however, and, as a result, techniques have been developed to address these difficulties multidimensionally. Such approaches have been shown to be an efficient way of identifying the areas affected in multi-cancer cases in order to define the treatment area. Multi-cancer methods that facilitate a certain degree of competence are thus required. This study tested storing MRI brain scans in a multidimensional image database, which is a significant departure from past studies, as a way to improve the efficacy, efficiency, and sensitivity of cancer detection. The evaluation findings offered success rates for cancer diagnoses of 99.08%, 99.87%, 94%;97.08%, 98.3%, and 93.38% sensitivity;the success rates of in particular were 99.99%;98.23%, 99.53%, and 99.98%. © 2023 Slovene Society Informatika. All rights reserved.

关键词： Image segmentation

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PQS-AKA: Post-Quantum Secure Lightweight Authenticated Lattice-Based Key Agreement for IoT Communications 4th

PQS-AKA: Post-Quantum Secure Lightweight Authenticated Latti...

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4th International Conference on Emerging Trends and Technologies on Intelligent Systems, ETTIS 2024

作者： Singh, Ajeet Tentu, Appala Naidu Rao, K. Gangadhara Department of Computer Science and Engineering Acharya Nagarjuna University Andhra Pradesh Guntur522 510 India C.R. Rao Advanced Institute of Mathematics Statistics and Computer Science University of Hyderabad Campus Telangana Hyderabad500 046 India

ISBN: (纸本)9789819757022

As the Internet of Things (IoT) ecosystem expands, the need for secure and private communication over public networks becomes increasingly crucial. A fundamental component of this security paradigm is the establishment of a secret session key among IoT devices, enabling the secure transmission of confidential data over the Internet. In this paper, we introduce a Post-Quantum Secure Lightweight Authenticated Lattice-based Key Agreement (PQS-AKA) protocol, specifically developed for the rapidly evolving IoT ecosystem. Traditional Authenticated Key Agreement (AKA) protocols, predominantly based on concepts like Diffie–Hellman or prime factorization, are becoming obsolete due to their vulnerability to quantum computing advancements and their inefficiency in the resource-constrained IoT environment. Current quantum-resistant methods, although leveraging lattice cryptography for security, are impeded by issues such as high certificate management overheads and substantial communication and computational costs. PQS-AKA not only offers a defense against the threats posed by quantum computing through the inherent hardness of lattice problems but also simplifies the process by removing the need for extensive certificate management. The core security strength of our protocol is thoroughly evaluated under the random-natured oracle model, demonstrating its robustness against both present and future quantum computing attacks. Performance evaluations reveal that the proposed protocol offers a significant improvement over existing solutions in terms of efficiency and practicality, making it a viable and secure choice for industrial IoT applications. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Quantum computers

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