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2023 International Conference on Culture-Oriented science and technology, CoST 2023

作者： Yin, Bo Chen, Yi Chen, Hang Wei, Xuetao School of Computer and Communication Engineering Changsha University of Science and Technology Changsha China Southern University of Science and Technology Department of Computer Science and Engineering Shenzhen China

ISBN: (纸本)9798350357998

The valuation of property rights of cultural products relies on professional knowledge and experience. Existing valuation approaches are vulnerable to third parties, leading to price manipulation. Moreover, neural network-based valuation methods suffer from low interpretability. In this paper, we propose a crowdsourcing-based framework for the valuation of cultural products. The framework chooses high-quality crowdsourcing workers to rate cultural products on 'computer-hard' indicators. We propose a confidence-aware rating model that aggregates ratings from multiple workers with a confidence level. The rating model can also provide a qualitative description of the performance of a culture product on each indicator. We use machine learning methods to predict the value based on the ratings of multiple indicators. Extensive experiments show the promising results of our proposed framework. © 2023 IEEE.

关键词： Crowdsourcing

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Plant Leaf Disease Classification Using Optimal Tuned Hybrid LSTM-CNN Model

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SN computer science 2023年第6期4卷 710页

作者： Patil, Manoj A. Manohar, M. Department of Computer Science and Engineering Christ (Deemed to be University) School of Engineering and Technology Kengeri Karnataka Bengaluru 560074 India Department of Computer Science and Engineering G. Narayanamma Institute of Technology and Science Shaikpet Telangana Hyderabad 500104 India

Tomatoes are widely cultivated and consumed worldwide and are susceptible to various leaf diseases during their growth. Therefore, early detection and prediction of leaf diseases in tomato crops are crucial. Farmers can take proactive measures to prevent the spread and minimize the impact on crop yield and quality by identifying leaf diseases in their early stages. Several Machine Learning (ML) and Deep Learning (DL) frameworks have been developed recently to identify leaf diseases. This research presents an efficient deep-learning approach based on a hybrid classifier by optimizing the CNN and LSTM models, which helps to enhance classification accuracy. Initially, Median Filtering (MF) is used for leaf image pre-processing. Then, an improved watershed approach is used for segmenting the leaf images. Subsequently, enhanced Local Gabor Pattern (LGP) and statistical and color features are extracted. An optimized CNN and LSTM are used for classification, and the weights are tuned using the SISS-OB (Self Improved Shark Smell With Opposition Behavior) algorithm. Finally, we have analyzed the performance using various measures. Since we have done segmentation, feature extraction, and optimization improvisations, our proposed methodology results are higher than other available methods and existing works. The results obtained at Learning Percentage (LP) is 90% which is far superior to those obtained at other LPs. The FNR (False Negative Rate) is much lower (0.05) at the 90th LP. The proposed model achieved better classification performance in terms of Accuracy of 97.13%, Sensitivity of 95.09%, Specificity of 95.24%, Precision of 94.31%, F measure of 96.71% and MCC 87.34%. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： CNN Deep Learning Hybrid classifier LSTM SISS-OB

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Blockchain in Concurrent Green IoT-Based Agriculture: Discussion, Analysis, and Implementation 6th

Blockchain in Concurrent Green IoT-Based Agriculture: Discus...

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6th International Conference on Intelligent Computing and Optimization, ICO 2023

作者： Alam, Md Janay Choudhury, Ashiquzzaman Al Maksud, Kazi Sifat Reza, Ahmed Wasif Arefin, Mohammad Shamsul Department of Computer Science and Engineering East West University Dhaka Bangladesh Department of Computer Science and Engineering Chittagong University of Engineering and Technology Chittagong Bangladesh

ISBN: (纸本)9783031503269

The adaptation of IoT-based technology in agriculture is increasing rapidly in recent times. IoT devices are of great assistance to farmers in monitoring dairy farms, greenhouses, and other agricultural facilities. The most crucial component of an IoT device is ensuring data security and privacy. Data leakage can have significant negative consequences as it may result in financial loss for farmers. It is important to prioritize data security to protect the interests of farmers and ensure the success of their agricultural endeavors. Blockchain technology can be a solution to secure data transactions in the network layer. With the concept of private and public key pairs, data hashing, and decentralized systems;important and sensitive data will be secured. In this study, we discuss the structure of a three-layer architecture for green IoT technology and identify the privacy issues presented in each layer. Then we propose a solution by implementing a secure Ethereum-based blockchain system for data transactions at the network layer. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Losses

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Effective Fault Prediction Techniques for the Green Cloud Computing Environment Applying Machine Learning to Enhance Network Management 6th

Effective Fault Prediction Techniques for the Green Cloud C...

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6th International Conference on Intelligent Computing and Optimization, ICO 2023

作者： Tamim, Hasnath Ahmed Hossain, Md. Sagor Asif, Md. Asif Uzzaman Uddin, Borhan Reza, Ahmed Wasif Arefin, Mohammad Shamsul Department of Computer Science and Engineering East West University Dhaka Bangladesh Department of Computer Science and Engineering Chittagong University of Engineering and Technology Chattogram Bangladesh

ISBN: (纸本)9783031362453

Network errors are a significant issue in cloud computing. Occasionally, issues with cloud networks may cause productivity to drop considerably. The primary objective of this research is to employ machine learning techniques for predicting failures in Cloud Networks, enabling prompt identification of issues. The author attempted to determine the most accurate method, the Random Forest, that matches our model. We trained our model using gradient boosting, decision trees, K-neighbors, Random forests, SVC, and logistic regression. Then, by analyzing data as performance indicators, resource use, and energy consumption, random forests may be employed in a green cloud computing environment to forecast upcoming flaws. The system can recognize patterns and correlations in data that may indicate an issue and generate predictions for future forecasting based on these patterns, with an accuracy of 80.825479%. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Cloud computing

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A trust-based clustering approach to form stable clusters in vehicular ad hoc networks

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Journal of Ambient Intelligence and Humanized Computing 2023年第12期14卷 16209-16228页

作者： Singh, Dinesh Maurya, Ashish Kumar Ranvijay Yadav, Rama Shankar Department of Computer Science and Engineering Motilal Nehru National Institute of Technology Allahabad Uttar Pradesh Prayagraj211004 India

Over the last two decades, vehicular ad hoc networks (VANETs) have evolved to disseminate real-time traffic information, emergency information, and multimedia data to vehicles on highways and urban roads. Due to vehicles’ high-speed mobility, the topology in VANETs changes dynamically and causes instability in networks. It further degrades the performance of vehicular applications and consumes network resources. Clustering is one of the approaches in VANETs that is utilized to achieve stability. This paper proposes a Trust-based Stable Clustering (TSC) approach to form stable clusters in VANETs. This approach encompasses a significant cluster head election, cluster joining, and cluster leaving algorithms. The cluster head election algorithm forms stable clusters based on several parameters such as vehicles’ trust value, degree, position, remaining travel time, and speed. The cluster joining and cluster leaving algorithms improve the stability of formed clusters by reducing the cluster reformation frequency and selecting alternate cluster heads. The performance of the TSC is compared with the well-known clustering methods on different parameters such as average cluster head duration, number of state changes, and number of cluster formation messages. The results show that the TSC outperforms than the compared approaches. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Vehicular ad hoc networks

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Empowering Resilience in Post-Disaster Communication with Low-End Communication Devices 26

Empowering Resilience in Post-Disaster Communication with Lo...

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26th International Conference on computer and Information technology, ICCIT 2023

作者： Sarkar, Ratna R. Islam, Md.Nazrul Islam, Rabiul Hasan, Md. Shiblu Zahidur Rahman, Mohammad Jahangirnagar University Department of Computer Science and Engineering Savar Bangladesh Atish Dipankar University of Science and Technology Department of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9798350359015

Natural or man-made disasters result in significant loss of life, property damage, disruption of communication networks, and immense suffering for survivors. When a disaster strikes, the communication infrastructure is frequently rendered unreliable or completely inoperable. In such cases, it is critical to establish emergency communication to lessen the aftermath of the disaster, rescue those in need, and accelerate relief efforts. This paper presents a communication model to empower resilience in communication after a disaster and also relief and rescue efforts. This model divides communication strategies into two categories namely communication between victims and rescuers (V2R) and communication among rescuers (R2R). This model collects victims' urgent aid messages, allowing rescuers to respond quickly and increase the speed of rescue and relief operations. Rescuers in the second strategy are able to communicate with one another. When combined, these two strategies form the model, ultimately leading to the acceleration of comprehensive rescue and relief operations and the mitigation of post-disaster consequences. © 2023 IEEE.

关键词： communication model post-disaster R2R Resilience V2R

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X-ray Image Analysis for Aided Detection of COVID-19

X-ray Image Analysis for Aided Detection of COVID-19

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2024 IEEE International Conference on Intelligent Computing and Emerging Communication Technologies, ICEC 2024

作者： Sharma, Avinash Barua, Tarkeshwar Garg, Prachi Saxsena, Rini Goyel, Parul Singh Parmar, Vivek Chandigarh Group of Colleges Department of Computer Science and Engineering Chandigarh Engineering 140307 India Roorkee Institute of Technology Department of Computer Science and Engineering Roorkee India Mmec Department of Computer Science and Engineering Mullana India M. M. Engineering College Maharishi Markandeshwar Mullana Computer Science & Engineering Haryana Ambala1307 India Chitkara University Department of Cse India

ISBN: (纸本)9798331508432

The COVID-19 epiphytotic highlighted the necessity for speedy and accessible identification instruments. Chest X-rays offer a readily available and relatively inexpensive Tomography proficiency. This stuff explores the potential of X-ray image analysis using Profound learning algorithmic program for the aided detection of COVID-19 contagions. While chest X-rays and CT scans are valuable instruments for COVID-19 diagnosis, manual analysis is time-consuming and error-prone. Profound learning, a powerful subset of artificial intelligence[10], offers promising solutions for automated and accurate COVID-19 detection from these Tomography modalities. This stuff reviews key investigation findings on Profound learning-based classification of COVID-19 using chest X-rays and CT scans. We explore the current state-of-the-art advancements, discuss remaining challenges, and outline potential future directions in this crucial field. Containing COVID-19 relies heavily on isolating infected individuals and widespread inoculation. While RT-PCR tests remain the gold standard for diagnosis, delays in processing limit their effectiveness. Additionally, post-recovery positive tests raise concerns about accuracy [7]. Vaccines offer protection, but breakthrough contagions occur. Developing highly effective, long-lasting vaccines and achieving planetary immunization face significant logistical hurdles. Faster and more reliable identification instruments are crucial to complement these ongoing efforts. © 2024 IEEE.

关键词： Vaccines

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Application of Support Vector Machine to Lassa Fever Diagnosis

Lecture Notes on Data Engineering and Communications Technol...

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Lecture Notes on Data engineering and Communications Technologies 2023年 180卷 165-177页

作者： Nwankwo, Wilson Adigwe, Wilfred Umezuruike, Chinecherem Acheme, Ijegwa D. Nwankwo, Chukwuemeka Pascal Ojei, Emmanuel Oghorodi, Duke Department of Computer Science Edo State University Uzairue Nigeria Department of Computer Science Delta State University of Science and Technology Ozoro Nigeria College of Computing and Communication Studies Bowen University Iwo Nigeria Department of Software Engineering Delta State University of Science and Technology Ozoro Nigeria Department of Cyber Security Delta State University of Science and Technology Ozoro Nigeria

Lassa fever is a type of viral hemorrhagic fever with high fatality rate. Precise and prompt diagnosis and successful treatment of this disease is very important in the control and prevention of the spread as unrestricted spread can easily erupt into an epidemic. An insightful decision support system can help make quick and more exact findings on this disease. In this paper, an intelligent model is proposed for predicting Lassa fever is proposed. The Lassa fever dataset was extracted from the clinical records of patients from a known Specialist Teaching Hospital in Southern Nigeria. The relevant feature variables were identified and encoded in a manner appropriate for use in algorithmic analysis. The support vector classifier was used to develop a characterization model for the disease. Assessment and evaluation were based on exactness, accuracy, and affectability. Findings showed that the proposed system could improve and encourage quick decision-making as it utilizes real historical data to effectively characterize Lassa fever spread as well as promote the timely treatment and management of patients. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Support vector machines

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AsPeer: Method to Self and Peer Assessment in Large Online Design Classes

International Journal of Emerging Technologies in Learning

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International Journal of Emerging Technologies in Learning 2023年第10期18卷 19-38页

作者： Sankaran, Hariesh K. Shanmugam, Raju Shah, Bhavesh Ahmad, Sultan Abdeljaber, Hikmat A. M. Nazeer, Jabeen Karnavati University Gujrat Gandhinagar India Department of Computer Science College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz University Alkharj Saudi Arabia Department of Computer Science Faculty of Information Technology Applied Science Private University Amman Jordan

Peer and self-assessment open opportunities to scale assessments in online classrooms. This article reports our experiences of using AsPeer-peer assessment system, with two iterations of a university online class. We observed that peer grades highly correlated with staff assigned grades. It was recorded that, the peer grade of all student submissions within range of instructor grade averaged to 21.0% and that within the next 2 ranges was 49.0%. We performed three experiments to improve accuracy of peer grading. First, we observed grading bias and introduced a data driven feedback mechanism to inform peers of it. Students aided by feedback were mindful and performed grading with better accuracy. Second, we observed that the rubric lacked efficiency in translating intent to students. Simplified guiding questions improved accuracy in assessment by 89% students. Third, we encouraged peers to provide personalized qualitative feedback along with rating. We provided them with feedback snippets that addressed common issues. 64% students responded that the snippets helped them to critically look at submissions before rating © 2023, International Journal of Emerging Technologies in *** Rights Reserved.

关键词： Students

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Relative Power Loss Analysis of Poly-Si PV Panels: An Overview in Eastern Indian Climatic Condition

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Journal of The Institution of Engineers (India): Series B 2022年第5期103卷 1775-1780页

作者： Pal, Sudipta Basu Das Bhattacharya, Konika Mukherjee, Dipankar Department of Computer Science Technology University of Engineering Management Kolkata India Department of Electrical Engineering Indian Institute of Engineering Science and Technology Shibpur India Department of Electronics and Telecommunication Engineering Indian Institute of Engineering Science and Technology Shibpur India

Renewable energies are generated from natural processes that are continuously renewed. Therefore, it is required to combine the PV modules either in series combinations or parallel combinations for developing the appropriate Photovoltaic (PV) panel/PV arrays. Several PV modules are connected in a series–parallel combination to constitute PV arrays. These PV arrays currently act as a decentralized source of D.C power for onward transmission. When identical wattages PV modules are coupled in a parallel or series connection, there must be a probability to the PV module's manufacturing process that gives rise to power loss. Mainly these types of losses are called Mismatch losses. Mismatch losses (power losses) in PV panels principally occur in terms of Relative Power Loss (RPL). Therefore, the authors carried out I–V characterizations of different technology of PV modules under varying insolation and temperatures. The main aim of the work is to develop the right kind of photovoltaic (PV) panel/PV arrays in a particular Eastern Indian climatic zone. From the authors' characterization of RPL, analysis was conducted to judge which technology is most suitable to build a large size array/BIPV. The experimental results reveal lower mismatch losses of poly-Si PV modules for crystalline silicon and amorphous silicon counterparts. © 2022, The Institution of Engineers (India).

关键词： Solar panels

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