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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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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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HybridNet: Integrating Multiple Approaches for Aerial Semantic Segmentation

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SN computer science 2024年第1期5卷 133页

作者： Chouhan, Avinash Sur, Arijit Chutia, Dibyajyoti Aggarwal, Shiv Prasad North Eastern Space Applications Centre Meghalaya Umiam 793103 India Department of Computer Science and Engineering Indian Institute of Technology Assam Guwahati 781039 India

In recent times, semantic segmentation for VHR aerial images has become an emerging research topic due to its widespread applications in disaster management, environmental monitoring, natural resource mapping, etc. The problem of semantic segmentation can be modeled as an image-to-image mapping problem where pixel-level classification is required. Pixel level classification is challenging for the high-resolution aerial image due to the presence of the tiny objects in low-frequency and more information details for such tiny objects required for dense semantic labeling. In general, encoder–decoder based architecture for semantic segmentation suffers from information loss due to the up and downsampling process. To handle this, we extend a high-resolution network with dense connection integration to preserve the original resolution and better parameter sharing. We also incorporate a lightweight self-attention module for positional attention, which results in better segmentation maps. Additionally, we use a generalized Hough transform based deep voting module for pixel dependencies extraction. Experimental results reveal that the proposed model achieves the best mean intersection over union and overall accuracy in local and benchmark evaluation on the Vaihingen and Potsdam datasets. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： High-resolution connection Hough voting Semantic segmentation

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Assessing the Impact of Migration from SOA to Microservices Architecture

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SN computer science 2023年第5期4卷 577页

作者： Raj, Vinay Bhukya, Hanumanthu Department of Computer Applications National Institute of Technology Tiruchirappalli Tamil Nadu Tiruchirappalli 620015 India Department of Computer Science and Engineering (Networks) Kakatiya Institute of Technology and Science Telangana Warangal 506015 India

Microservices has become a buzzword in IT and large enterprise firms such as Netflix, Twitter, Spotify and others have started to design their applications by adopting this new architectural style. A few organizations have started migrating their traditional monolithic and SOA-based applications to microservices in order to benefit from the features of new style. Software architects, on the other hand, are in chaos whether to adopt this new style or not as they are unaware of the pros and cons of microservices architecture. Also, the impact of the migration on SOA-based applications in terms of performance and complexity is unknown, leading to dilemma on the migration process. In this paper, a study of the migration impact on the existing SOA based applications to microservices is presented. For this study, change propagation probability and architectural stability metrics are used to examine the effect of migrating a SOA-based application to microservices architecture. The proposed approach is illustrated on a case study application designed using SOA and then migrated to microservices. It is observed from the results that though the impact of migration is high, migrating to microservices has significant benefits and it is best suitable for large enterprise applications. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Change propagation Microservices Migration Service oriented architecture Stability

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Future Database Technologies for Big Data Analytics

Future Database Technologies for Big Data Analytics

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2023 International Conference on Intelligent Systems for Communication, IoT and Security, ICISCoIS 2023

作者： Rani, Geeta Sharma, Taruna Sharma, Avinash Chitkara University Institute of Engineering and Technology Dept of Computer Applications Punjab India Maharishi Markandeshwar Engineering College Department of Computer Science and Engineering Mullana India

ISBN: (纸本)9798350335835

Data is valuable asset for organizations but it cannot be used directly for business decision making. It needs to be processed in a form that is easily understandable and can to be used by decision makers. The database technology is continuously changing as the data is growing at a rapid date and business requirements change with time. With the advent of IOT, social media and other technology advancements, data become more complex in terms of volume, variety and veracity. Traditional technology can't be used for handling this data due to its complex and dynamic nature of Big Data. So more advanced and sophisticated tools are essential to handle complex nature of data. In this paper the various database tools are discussed and analysed by different vendors which are suitable for big data Analytics. © 2023 IEEE.

关键词： Big data

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An Empirical Study of Deep Learning Techniques for Diabetic Retinopathy Detection 3

An Empirical Study of Deep Learning Techniques for Diabetic ...

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3rd International Conference on Innovative Sustainable Computational Technologies, CISCT 2023

作者： Pandey, Trilok Nath Pattnaik, Shyam Sankalp Sharma, Sanyam Ravalekar, Vishvajeet Dash, Bibhuti Bhusan Patra, Sudhansu Shekhar School of Computer Science and Engineering VIT Chennai India School of Computer Applications Kalinga Institute of Industrial Technology Bhubaneswar India

ISBN: (纸本)9798350303360

Presently, a competent professional must perform a thorough manual examination of digital color fundus photographs of the retina in order to diagnose diabetic retinopathy (DR). The delayed results result in missed evaluation, inaccurate communication, and postponed therapy by the time technicians complete their evaluations, which is often a day or two later. By looking for lesions connected to the vascular anomalies brought on by the disease, doctors can spot DR. This strategy works well, but it uses a lot of resources. Where DR detection is most important and there is a high prevalence of diabetes in the area, there is typically a lack of the requisite information and resources. As the number of persons with diabetes increases, the infrastructure required to prevent DR-related blindness will become increasingly more insufficient. In order to diagnose and classify diabetic retinopathy, this paper aims to develop and deploy image processing and ML algorithms to analyze fundus images. © 2023 IEEE.

关键词： Deep learning

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Cloud-Based Deep Learning for Weather Forecasting

Cloud-Based Deep Learning for Weather Forecasting

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2025 International Conference on Pervasive Computational Technologies, ICPCT 2025

作者： Sharma, Rupak Tyagi, Pallavi Vajpayee, Pooja Gangwar, Deepti Tyagi, Umang Saraswat, Birendra Kumar Srm Institute of Science and Technology Department of Computer Applications NCR Campus UP Modinagar India Raj Kumar Goel Institute of Technology Computer Science & Engineering UP Ghaziabad India Galgotias University School of Computer Science and Engineering Greater Noida India Raj Kumar Goel Institute of Technology Department of Ece UP Ghaziabad India

ISBN: (纸本)9798331508685

Deep learning is a robust framework for solving complex prediction and forecasting problems from multiple fields, such as weather sciences. Due to the abundance and higher resolution of data available, cloud-based deep learning offers a compelling approach for achieving promising gains in accuracy and efficiency regarding weather forecasting. A more pressing priority in the face of worsening weather events requires faster and more reliable forecasting. Deep learning in the cloud is implemented by training deep neural networks on large datasets stored in a public or private cloud and subsequently using these pre-trained models to predict new data. This encourages using large-scale computational resources, such as those necessary to tackle the heavy data needed for precise meteorological forecasts. Such deep learning models have the flexibility and scalability required to adapt and tune them for specific weather patterns, which can then run on a cloud, enabling readiness as demanded. In this work, 3, x, y present a scalable deep learning-based approach to extended-range temperature prediction on high-performance cloud resources. We use the model to train on historical weather data combined with real-time observed weather from various sources. © 2025 IEEE.

关键词： Prediction models

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Video Anomaly Detection Based on HSOE-FAST Modified Deep Neural Network

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SN computer science 2024年第5期5卷 588页

作者： Gupta, Anil Kumar Sharma, Rupak Ojha, Rudra Pratap Department of Computer Science and Engineering SRM Institute of Science and Technology NCR Campus Modinagar Uttar Pradesh Ghaziabad India Department of Computer Applications SRM Institute of Science and Technology NCR Campus Modinagar Uttar Pradesh Ghaziabad India Department of Computer Science and Engineering G.L. Bajaj Institute of Technology and Management (GLBITM) (Affiliated to AKTU) Uttar Pradesh Greater Noida India

In recent times, video surveillance has become indispensable for public security, leveraging computer vision advancements to analyze and comprehend lengthy video feeds. Anomaly detection and classification stand out as crucial components of this technology. Anomaly detection's primary objective is to swiftly identify irregularities within a given timeframe. It is promising to use Deep Neural Network (DNN) approaches for anomaly detection because they combine the ideas of deep learning and reinforcement learning, enabling artificial agents to learn from and gain insights from real-world data. A modified DNN (Deep Neural Network) technique known as HSOE-FAST (Histo sigmoid of Orientation and Enthalpy with Fast Accelerated Segment Test) was proposed in this study. From the dataset, the input is obtained. Initially, the input is pre-processed using a Gaussian filter followed by the feature extraction using the HSOE-FAST method and finally classification is done using the modified DNN approach. Compared to other approaches, our suggested solution achieves an accuracy of almost 99% while overcoming the shortcomings of the existing methodologies. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： DNN Enthalpy Fast Accelerated Segment Test Histo sigmoid of Orientation Video anomaly detection

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Enhancing waste sorting and recycling efficiency: robust deep learning-based approach for classification and detection

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Neural Computing and applications 2025年第6期37卷 4567-4583页

作者： Sayem, Faizul Rakib Islam, Md. Sakib Bin Naznine, Mansura Nashbat, Mohammad Hasan-Zia, Mazhar Kunju, Ali K Ansaruddin Khandakar, Amith Ashraf, Azad Majid, Molla Ehsanul Kashem, Saad Bin Abul Chowdhury, Muhammad E. H. Department of Electrical and Electronic Engineering University of Dhaka Dhaka Bangladesh Department of Biomedical Engineering Military Institute of Science and Technology Dhaka Bangladesh Department of Computer Science & Engineering Rajshahi University of Engineering & Technology Rajshahi6204 Bangladesh Chemical Engineering Department University of Doha for Science and Technology Doha Qatar Department of Electrical Engineering Qatar University Doha2713 Qatar Computer Applications Department Academic Bridge Program Qatar Foundation Doha Qatar Department of Computing Science AFG College With the University of Aberdeen Doha Qatar

Given the severity of waste pollution as a major environmental concern, intelligent and sustainable waste management is becoming increasingly crucial in both developed and developing countries. The material composition and volume of urban solid waste are key considerations in processing, managing, and utilizing city waste. Deep learning technologies have emerged as viable solutions to address waste management issues by reducing labor costs and automating complex tasks. However, the limited number of trash image categories and the inadequacy of existing datasets have constrained the proper evaluation of machine learning model performance across a large number of waste classes. In this paper, we present robust waste image classification and object detection studies using deep learning models, utilizing 28 distinct recyclable categories of waste images comprising a total of 10,406 images. For the waste classification task, we proposed a novel dual-stream network that outperformed several state-of-the-art models, achieving an overall classification accuracy of 83.11%. Additionally, we introduced the GELAN-E (generalized efficient layer aggregation network) model for waste object detection tasks, obtaining a mean average precision (mAP50) of 63%, surpassing other state-of-the-art detection models. These advancements demonstrate significant progress in the field of intelligent waste management, paving the way for more efficient and effective solutions. © The Author(s) 2024.

关键词： Deep learning for waste management Intelligent waste management Object detection in waste management Sustainable waste processing Urban solid waste Waste image classification

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RECRIoT-MAC: A Reliable and Energy Efficient Resource Management for Internet of Things Based Cognitive Radio Multi- Channel MAC Protocol

RECRIoT-MAC: A Reliable and Energy Efficient Resource Manage...

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2024 International Conference on Communication, computer sciences and engineering, IC3SE 2024

作者： Agrawal, Navin Kumar Kumar, Sandeep Bisht, Girish Singh Srivastav, Akanksha Bansla, Vandna Jain, Arpit Gla University Department Of Computer Engineering And Applications U.P. Mathura India Tula's Institute Department Of Computer Science And Engineering Uttarakhand Dehradun India Shivalik Engineering College Department Of Computer Science And Engineering Uttarakhand Dehradun India Koneru Lakshmaiah Education Foundation Department Of Computer Science And Engineering AP Vaddeswaram India

ISBN: (纸本)9798350366846

Currently, the overcrowding of unlicensed spectrum bands is causing severe interference to IoT devices embedded with sensors. CR-IoT networks apply cognitive radio capabilities to the internet of things to solve the spectrum scarcity issue in conventional IoT networks. An CR radio detects available spectrum channels by dynamically modifying its programming and configuration. IoT, smart cities, and 5G all benefit from cognitive radio applications. A cognitive radio application is useful for IoT, smart cities, and 5G. Compared to benchmark Cognitive Radio MAC (CR- MAC) protocols, the proposed protocol is more reliable, intelligent, and smart. CR technology for IoT applications can be advanced by us in order to make IoT more affordable and applicable. It is demonstrated by comparing the proposed RECRIoT-MAC protocol with benchmark CR-MAC protocols that is more energy efficient and consumes less communication time. This paper examines RECRIoT-MAC's energy consumption and throughput. © 2024 IEEE.

关键词： Cognitive radio

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