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2nd IEEE International Conference on IoT, Communication and Automation Technology, ICICAT 2024

作者： Asthana, Sameer Chaturvedi, Ravi Prakash Mishra, Annu Nayyar, Poorva Shaliyar, Md. Greater Noida Institute of Technology Department of Computer Science and Engineering Greater Noida India Sharda School of Engineering and Technology Department of Computer Science and Applications Greater Noida India Sharda School of Engineering and Technology Department of Computer Science and Engineering Greater Noida India

ISBN: (纸本)9798350368109

This article summarizes the Blockchain technology along with the artificial intelligence. The cutting edge technology is described in a elaborated manner along with its advantages, disadvantages and its impact with the artificial intelligence. There are various sectors in which it is used and the outcomes are discussed. The proficiency of blockchain has proved its reliability and threshold in different domains. © 2024 IEEE.

关键词： artificial intelligence blockchain ledger security

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Low-Cost Weather Monitoring System with Intelligent Design and XGBoost Regression

Low-Cost Weather Monitoring System with Intelligent Design a...

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2023 International Conference on Evolutionary Algorithms and Soft Computing Techniques, EASCT 2023

作者： Vishnu Vilashini, S. Kishore, S. Nair, Rekha. R. Babu, Tina Bhadani, Anant Thanushree, G.R. Dixit, Anuj Kumar Dayananda Sagar University School of Engineering Department of Computer Applications Bengaluru India Nitte Institute of Professional Education Department of Computer Applications Karnataka India Allaince University Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350313413

Weather monitoring is a critical aspect of weather forecasting and analysis, providing essential data for decision-making in various sectors. However, traditional weather monitoring systems face limitations such as high costs, limited accuracy, and inadequate coverage. The proposed system addresses these challenges by utilizing the Internet of Things (IoT) technology and NodeMCU, a low-cost microcontroller. The novelty of this work lies in the implementation of a cost-effective weather monitoring system that accurately forecasts upcoming weather conditions. Notably, the XGBoost Regression model emerges as the top performer, exhibiting exceptional accuracy with a mean absolute error (MAE) of 0.0213 indicating high accuracy in weather prediction. This innovative approach not only provides an affordable solution but also demonstrates the system's advanced capability to predict future weather patterns. Utilizing a NodeMCU board, real-time sensor data is gathered and transmitted to ThingSpeak's cloud server for analysis. This methodology has the potential for future applications in industries such as agriculture, transportation, and disaster management. © 2023 IEEE.

关键词： NodeMCU Real-time monitoring Recursive feature elimination ThingSpeak Weather prediction XGBoost Regression

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Optimizing Quality of Service in IoMT Through Fog Computing-Enabled Service Composition 13

Optimizing Quality of Service in IoMT Through Fog Computing-...

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13th International Conference on System Modeling and Advancement in Research Trends, SMART 2024

作者： Anand, Dinesh Kaur, Avinash Singh, Parminder School of Computer Applications Lovely Professional University Punjab Phagwara India Guru Nanak Dev Engineering College Department of Computer Applications Punjab Ludhiana India School of Computer Science and Engineering Lovely Professional University Punjab Phagwara India

ISBN: (纸本)9798350380583

The Internet of Medical Things (IoMT) has emerged as a paradigm shift in healthcare, allowing for remote patient monitoring and personalized treatments. However, due to the resource limitations of medical devices, the vital nature of health- care data, and the necessity for real-time response, guaranteeing Quality of Service (QoS) in IoMT systems offers considerable issues. This research presents a technique for addressing these difficulties in IoMT by exploiting Fog Computing-Enabled Service Composition. The issue statement centers on the need to improve QoS in IoMT systems, which includes response time, reliability, and energy consumption. Existing solutions struggle to match the severe criteria of IoMT, where service deployment, orchestration, and data processing must be performed in a fast, energy-efficient, and reliable way. To address these concerns, we propose a set of techniques that combine Fog Computing with service composition to optimize QoS in IoMT. The framework is proposed to incorporate dynamics service orchestration, context ware decision-making and balance the load into the IoMT systems to allow service composition at network edge. The implementation of proposed technique is done and demonstrates how computing nodes are worked inside the IoMT system to enable dynamic service composition. Our results show improvements in the QoS parameters like response times, optimized energy consumption and enhanced reliability. © 2024 IEEE.

关键词： Fog Computing Internet of Medical Things Quality of Service Service Composition

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Impact of time delay and cooperation strategy on the stability of a predator-prey model with Holling type III functional response

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International Journal of Biomathematics 2023年第3期16卷 165-193页

作者： I.Benamara A.El Abdllaouit J.Mikramt Laboratory of Mathematics Computer Science&Applications-Security of Information Department of Mathematics Faculty of Sciences Mohammed V University in RabatMorocco

In this paper,we propose and analyze a delayed predator-prey model with Holling type III functional response taking into account cooperation behavior in *** time delay is introduced in the attack rate to represent the time necessary to trigger the *** analytical result is followed by an ecological *** investigate the effect of hunting cooperation on both the number and the level of positive steady *** observe that the level of the positive equilibrium decreases when increasing the hunting cooperation ***,we study the impact of the delay as well as the cooperation in hunting on the dynamics of the *** prove that the presence of delay in the attack rate induces stability switches around the coexisting equilibrium when predators *** addition,we consider the discrete delay as a bifurcation parameter and prove that the model undergoes a Hopf-bifurcation at the coexisting equilibrium when the delay crosses some critical *** simulations are presented to confirm our analytical findings.

关键词： Predator-prey system Holling type III discrete delay cooperative hunting Hopf-bifurcation switch of stability

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Leveraging Technology to Empower Millet Farmers: A Retrieval-Augmented Generation Approach with Large Language Models 5

Leveraging Technology to Empower Millet Farmers: A Retrieval...

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5th IEEE Global Conference for Advancement in Technology, GCAT 2024

作者： Anju, S. Krishnan, Anuja G. Veena, G. Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Applications Kerala Amritapuri India

ISBN: (纸本)9798350376685

This research addresses the critical need to bridge the gap between traditional agricultural knowledge and modern findings, particularly focusing on millet cultivation in India. Millet, being a staple grain in the region, holds significant importance for both farmers and agricultural researchers. However, the lack of accessible technology exacerbates the dissemination of crucial information to farmers. To tackle this challenge, our study proposes a comprehensive solution leveraging cutting-edge technologies such as Large Language Models (LLMs), Prompting, and vector database management. The core of our approach centers on the utilization of RAG techniques, which involves scraping and segmenting millet-related data into manageable chunks. These chunks are then transformed into vectors using LLMs and stored in the vector database. Similarity measures such as cosine similarity to obtain relevant vectors in response to questions posed by farmers. Prompting is then employed to provide responses that understood by humans. Crucially, the technology prevents the spread of false information by refusing to respond to questions that are not related to agriculture, thus guaranteeing accuracy. © 2024 IEEE.

关键词： Information management

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AI-Powered Model for Intelligent Resume Recommendation and Feedback 15

AI-Powered Model for Intelligent Resume Recommendation and F...

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

作者： Mishra, Abhishek Singh, Siddharth Jisha, R.C. Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Applications Kerala Amritapuri India

ISBN: (纸本)9798350370249

The research aims to develop a system that is capable of suggesting the most suitable resumes based on the job requirements provided by recruiters through uploaded files. The proposed System approach utilizes the Bert Model to enhance the precision and relevance of job recommendations, and ensure a seamless alignment between skills and job requirements. Furthermore, that system offers constructive feedback to candidates whose skills may not match the specified job requirements, going beyond the traditional job recommendation process. This feedback mechanism gives valuable insights for personal and professional growth, and it is also promoting a transparent and constructive interaction between candidates and the hiring process. In addition to providing instant resume recommendations and feedback, our model will also anticipates upcoming future job opportunities based on the candidate's skills set. This predictive feature is allowing candidate to strategically plan their career paths and stay ahead of evolving industry demands. © 2024 IEEE.

关键词： Contrastive Learning

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An Efficient Approach for Semantic Segmentation of Salt Domes in Seismic Images Using Improved UNET Architecture

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Journal of The Institution of Engineers (India): Series B 2023年第3期104卷 569-578页

作者： Bodapati, Jyostna Devi Sajja, RamaKrishna Naralasetti, Veeranjaneyulu Department of Advanced Computer Science and Engineering Vignan’s Foundation for Science Technology and Research Vadlamudi India IAENG Life Member Andhra Pradesh Guntur India Department of Information Technology and Computer Applications Vignan’s Foundation for Science Technology and Research Vadlamudi India

Many areas of Earth’s surface with large accumulations of gas and oil even have huge deposits of salt under the surface. Exploring such deposits helps many countries to increase the storage capacity of their Petroleum reserves and explore new ones. But finding such deposits is a herculean task. Expert seismic imaging requires human interpretation of salt bodies. But this leads to very biased and highly variable translations. So the idea behind this paper is to build an approach that accurately and automatically identifies if the seismic image contains any region of salt deposit or not. If a surface is found to have salt deposits, then it may contain the accumulations of oil or gas and even the salt domes or caverns can be used as a storage site for already available petroleum or oil. Since semantic segmentation classifies every pixel in the given image to its class label, this can be used to segment the salt deposits from the provided seismic images. In this paper, we introduce a variation of UNet, a popular segmentation model, for seismic image segmentation. We have added a batch normalization layer following every convolution layer as a deeper network helps extract better features which turned out to be true. Some interesting findings of this work are that the augmentation works well as this avoids over-fitting of the model. Sharpening as a post-processing technique has come up with a considerable amount of rise in the performance. Here, we prefer to use the metric as Intersection over Union (IoU) instead of accuracy as it is not as affected by the class imbalances that are inherent in foreground/background segmentation tasks. With the proposed methodology, we achieve an averaged IoU of 85.6 which is far better compared to the IoU achieved with the Segnet approach which stands at 77. © 2023, The Institution of Engineers (India).

关键词： Image enhancement

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Multi-agent QoS-aware autonomic resource provisioning framework for elastic BPM in containerized multi-cloud environment

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Journal of Ambient Intelligence and Humanized Computing 2023年第9期14卷 12895-12920页

作者： Saif, Mufeed Ahmed Naji Niranjan, S.K. Murshed, Belal Abdullah Hezam Al-ariki, Hasib Daowd Esmail Abdulwahab, Hudhaifa Mohammed Department of Computer Applications Sri Jayachamarajendra College of Engineering VTU Mysore India Department of Studies in Computer Science University of Mysore Karnataka Mysore India Department of Computer Science College of Eng. &IT University of Amran Amran Yemen Department of Information Technology Faculty of Engineering and Information Technology Al-ata’a University for Science and Technology Taiz Yemen Department of Computer Networks Engineering and Technologies Sana’a Community College Sana’a Yemen Department of Computer Application Ramaiah Institute of Technology VTU Bangalore India

Cloud computing enables businesses to improve their market competitiveness, enabling instant and easy access to a pool of virtualized and distributed resources such as virtual machines (VM) and containers for executing their business operations efficiently. Though the cloud enables the deployment and management of business processes (BPs), it is challenging to deal with the enormous fluctuating resource demands and ensure smooth execution of business operations in containerized multi-cloud. Therefore, there is a need to ensure elastic provisioning of resources to tackle the over and under-provisioning problems and satisfy the objectives of cloud providers and end-users considering the quality of service (QoS) and service level agreement (SLA) constraints. In this article, an efficient multi-agent autonomic resource provisioning framework is proposed to ensure the effective execution of BPs in a containerized multi-cloud environment with guaranteed QoS. To improve the performance and ensure elastic resource provisioning, autonomic computing is utilized to monitor the resource usage and predict the future resource demands, then resources are scaled based on demand. Initially, the required resources for executing the incoming workloads are identified by clustering the workloads into CPU and I/O intensive, and the local agent achieves this with the help of an initialization algorithm and K-means clustering. Then, the analysis phase predicts the workload demand using the proposed enhanced deep stacked auto-encoder (EDSAE), further, the containers are scaled based on the prediction outcomes, finally, the multi-objective termite colony optimization (MOTCO) algorithm is used by the global agent to find suitable containers for executing the clustered workloads. The proposed framework has been implemented in the Container Cloudsim platform and evaluated using the business workload traces. The overall simulation results proved the effectiveness of the proposed approach compare

关键词： Quality of service

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Machine Learning for Recognition of Handwritten Devanagri Character 1

Machine Learning for Recognition of Handwritten Devanagri Ch...

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1st International Conference on Computing, sciences and Communications, ICCSC 2024

作者： Tyagi, Shilpa Dutta, Chiranjit Singh, Manu SRM Institute of Science and Technology Department of Computer Applications Uttar Pradesh Ghaziabad201204 India SRM Institute of Science and Technology Department of Computer Science & Engineering Uttar Pradesh Ghaziabad201204 India ABES Engineering College Department of Computer Science Uttar Pradesh Ghaziabad India

ISBN: (纸本)9798350353648

A framework known as optical character recognition is capable of converting handwritten or printed pictures into machine-editable structures. Several Indian dialects, including Hindi, Nepali, Marathi, Sindhi, and others, use the Devanagari script. In India, Hindi is the national language and the language that is spoken by most people. This script organises the development of the language. Nowadays, people are interested in gathering data in sophisticated configurations that are available in paper archives and then employing a search process to reuse this data later. In this study, we provide a novel approach to Devanagari script character recognition for printed Hindi characters. The principal objective of this research is to identify the consonants and the vowels, which can subsequently be used for recognizing the complicated inferred words. The primary focus of this project is on the identification of each individual consonant and vowel. Several pre-processing techniques, including feature extraction, segmentation, and classification, have been researched and used in this project to create an advanced Hindi OCR system. The K-NN classification technique was used in earlier research;however, in the work that is being suggested, we have employed a hybrid technique that combines neural networks with K-NN. The recognition rate of the suggested approach is 97.4%, whereas that of the existing techniques is 94.5%. This suggests that the approach which proposed here is superior to techniques employed in the method which already exist. © 2024 IEEE.

关键词： Support vector machines

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A Data Privacy-Preserving Model driven on Differential Approach in Cloud Environment

A Data Privacy-Preserving Model driven on Differential Appro...

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2023 International Conference on Green Energy, Computing and Intelligent Technology, GEn-CITy 2023

作者： Gupta, Rishabh Saxena, Deepika Singh, Ashutosh Kumar Department of Computer Science and Engineering The University of Aizu Aizuwakamatsu Japan Department of Computer Applications National Institute of Technology Kurukshetra India

ISBN: (纸本)9781839539596

With the wide range of cloud services, including flexibility, high scalability, and the least upfront capital investment, it also offers machine learning services that are utilized in various applications, such as medical diagnosis, risk analysis, and product recommendation. Since it has many advantages, many organizations move their data to the cloud platform for storage, processing, and distribution intentions. The classification service is offered by the cloud service provider so that owners can assess their data. It gathers the data from the owners' side for training the machine learning model and accomplishing the classification task. The cloud servers handled by a third party might not be totally trusted by the data owners. However, data privacy is a significant challenge among the critical barriers to preserving data while distributing it. A novel data protection model is proposed, driven on machine learning and differential approaches that carry out classification tasks while protecting the owners' data through noise addition. The model enables the different owners to store, share, and utilize data on the cloud platform. The experiments are carried out on Cleveland, New-Thyroid, and SA-Heart datasets to determine the effectiveness of the model in terms of accuracy, precision, recall, and F1-score. The results show that it achieves high accuracy, precision, recall, and F1-score up to 88.37%, 83.28%, 98.41%, and 82.91%, respectively. © The Institution of Engineering & Technology 2023.

关键词： Risk analysis

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