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

作者： Chadha, Shikha Ahmad, Salman Khursheed Sharma, Dolly Singh, Manu Srivastavaline, Anubhava Sharda University Department of Computer Science Greater Noida India School Of Computer Science & Engineering Galgotias University Department Of Computer Science Engineering Greater Noida India Graphic Era Hill University School of Computing Department Haldwani India ABES Engineering College Department of Computer Science UP Ghaziabad India

ISBN: (纸本)9798350353648

This research paper presents the development of a weather forecasting model that incorporates real-time data through Application Programming Interfaces. This model utilises simple algorithms to analyse meteorological parameters from diverse API data sources, aiming to improve forecast accuracy compared to traditional methods. Extensive datasets are used for training and validation, demonstrating the model's effectiveness in providing more precise predictions. The study highlights the significance of API integration in advancing weather forecasting methodologies and fostering innovation in the field. The model created has yielded highly reliable results with minimal error. © 2024 IEEE.

关键词： Prediction models

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Detecting Uteropelvic Junction Obstruction at an Early Stage using Ultrasound Images through an Interplanetary File System 5

Detecting Uteropelvic Junction Obstruction at an Early Stage...

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5th International Conference on Data Intelligence and Cognitive informatics, ICDICI 2024

作者： Kaviya, R. Prasad, Mridula Pavithra, S. Kapali, B. Suresh Chander Vellore Institute of Technology School of Computer Science and Engineering Chennai India Easwari Engineering College Department of Biomedical Engineering Chennai India

ISBN: (纸本)9798350389609

Ureteropelvic junction obstruction (UPJO) is a medical condition characterized by a blockage at the junction between the ureter and the kidney, leading to impaired urine flow and increased fluid pressure. If left untreated, UPJO can progress to hydronephrosis and potentially result in irreversible kidney damage. This paper presents a novel system for the early detection and severity grading of UPJO using Deep Learning computer-Aided Diagnosis (DL-CAD) techniques. The proposed approach leverages ultrasound imaging as a primary diagnostic tool, noted for its cost-effectiveness, absence of radiation, and non-invasive nature. To enhance the security and integrity of medical data transmission, we integrate blockchain technology into our framework. Our solution combines a DL- CAD model featuring an attention-based pyramid semantic segmentation network with a discrete wavelet transformation- processed residual classification network. An IoT device is employed to detect patient abnormalities and transmit this data to the diagnosis model via an InterPlanetary File System (IPFS). This decentralized storage solution facilitates secure data management across a peer-to-peer network. Ultimately, the proposed framework not only identifies the presence of UPJO but also assesses its severity, ensuring that patient data remains protected throughout the diagnostic process. © 2024 IEEE.

关键词： Semantic Segmentation

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The OCPT Mechanism: An Innovative Methodology to Erect the Language Understanding to Build the Enhanced STT Model 4

The OCPT Mechanism: An Innovative Methodology to Erect the L...

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4th International Conference on Ubiquitous Computing and Intelligent Information Systems, ICUIS 2024

作者： Saranya, V. Devi, T. Deepa, N. Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798331529635

Generative Pretrained Transformers (GPT) predict the sequence of words when building the language model in speech recognition. The traditional model inherits the high range biases during training the data, this issue leads to inappropriate outputs, the proposed research, the Optimized Constructive Pretrained Transformers(OCPT) mechanism emphasizes tuning GPT models specific to domain language modeling, accentuating the value of an organized approach that involves data preprocessing, model initialization, fine-tuning, and assessment. This model helps to build a more efficient and effective Speech-to-text model. The model obtained 97.12% accuracy on the validation set, compared with the AuGPT model which produces 91% accuracy, indicating the proposed model's ability to understand and generate contextually relevant text. During the training phase, the model was hyperparameter-tuned to improve performance metrics including learning rate, batch size, and training epochs. The Adam optimizer was important in accurately adjusting the model parameters to ensure convergence and stability. To avoid overfitting and ensure the model's generalizability, accuracy, and loss have been evaluated throughout the training and validation phases. The findings assessed the model's learning trajectories and performance indicators, demonstrating its efficacy. © 2024 IEEE.

关键词： Distribution transformers

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How international conflicts and global crises can intertwine and affect the sources and levels of air pollution in urban areas

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Environmental Science and Pollution Research 2024年第39期31卷 51619-51632页

作者： Ghaffarpasand, Omid Blake, Rhiannon Shalamzari, Zahra Davari School of Geography Earth and Environmental Sciences University of Birmingham Birmingham United Kingdom Department of Computer Engineering University of Isfahan Isfahan Iran

This paper analyses the intertwined impacts of the 2018 US sanctions on Iran and the COVID-19 pandemic (as examples of unplanned international conflicts and global crises) on the source and extent of air pollution in Tehran, the capital of Iran. The impacts are parametrized using the levels of criteria air pollutants (CAP) for 5 years (2015–2020), which were previously deweathered using the promising machine learning technique of Random Forest (RF). The absolute principal component scores-multiple linear regression (APCS-MLR) method and the bivariate polar plot (BPP) technique are used here to analyze the source apportionment profile of the city for the business as usual (BAU;2015 to 2018), sanctions (2019), and COVID-19 and sanctions (2020) intervals. The results show the severe impact of the 2018 US sanctions on Tehran’s air quality (AQ);O3, NO2, CO, PM2.5, and PM10 levels increased by 117%, 55%, 20%, 35%, and 10%, respectively, while SO2 levels decreased by 30%. The sanctions also triggered a number of events, such as the disruption of the high-grade fuel supply chain and the Mazut crisis, which directly or indirectly accelerated the photochemical production of local tropospheric ozone to some extent. Sanctions also disrupted Tehran’s AQ response to the pandemic, with CAP levels decreasing by only 2–7% during the pandemic. The ozone and PM10 BPPs show that the source apportionment profile of the city is dominated by local anthropogenic emission sources, especially urban transport, after the sanctions and the pandemic. Results also show that the impact of soft wars, such as the US sanctions against Iran, on urban air quality degradation is much stronger than that of hard wars, such as the Russia-Ukraine war. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Air quality

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Non-Contact Vital Prediction Using rPPG Signals 1

Non-Contact Vital Prediction Using rPPG Signals

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1st IEEE International Conference on Contemporary Computing and Communications, InC4 2023

作者： Madhu, Meghna Gurudas, V.R. Manjunath, Chinthakunta Naik, Praveen Kulkarni, Praveen School of Engineering and Technology Department of Computer Science and Engineering Bangalore India Dayananda Sagar University Department of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798350335774

In this paper, we present the clinical significance of various cardiac symptoms with the use of heart rate detection, ongoing monitoring and present emotions. The development of algorithms for remote photoplethysmography has drawn a lot of interest during the past decade (rPPG). As a result, using data gathered from the video feed, we can now precisely follow the heart rate of individuals who are still seated. rPPG algorithms have also been developed, in addition to technique based on hand-crafted characteristics. Deep learning techniques often need a lot of data to train on, but biomedical data frequently lacks real-world examples. The experiment described in this work, we looked at how illumination affected the rPPG signals' SNR. The findings show that the SNR in each RGB channel varies depending on the colour of the light source. Paper describes development in video filtering for recognising the comprehending human face emotions. In our method, emotions are deduced by identifying facial landmarks and analysing their placement. © 2023 IEEE.

关键词： Photoplethysmography

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Real-Time Monitoring and Optimization of Recycled Water Irrigation in Coffee Plantations using Bi-directional RNNs and IoT Sensors

Real-Time Monitoring and Optimization of Recycled Water Irri...

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2024 International Conference on Innovation and Intelligence for informatics, Computing, and Technologies, 3ICT 2024

作者： Raveena, S. Surendran, R. Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India

ISBN: (数字)9798331533137

ISBN: (纸本)9798331533137

The coffee firms relies heavily on sustainable water management measures due to water scarcity. Present study investigates the utilization of Bi-directional Recurrent Neural Networks (Bi-directional RNNs) combined with Internet of Things (IoT) sensors to monitor and optimize the irrigation of reclaimed water in coffee plants in real-time. The suggested system utilizes a range of IoT sensors to gather up-to-the-minute information on soil moisture, temperature, humidity, pH value, nutrient value, water quality, and potentially weather conditions. Data collected is transmitted through the IoT Lora WAN wireless network, which facilitates long-distance communication for plantations. Bi-directional recurrent neural networks (RNNs) examine the sequential sensor data to detect temporal relationships and determine the best watering schedules and possible irregularities. The proposed model suggests modifications accuracy of 95.66% to irrigation practices based use of water in a more effective manner. ©2024 IEEE.

关键词： Soil moisture

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Predicting Student Academic Performance using Machine Learning: Analyzing Socio-Economic and Personal Factors from Secondary Education in Portugal 4

Predicting Student Academic Performance using Machine Learni...

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4th International Conference on Technological Advancements in Computational Sciences, ICTACS 2024

作者： Sahu, Santosh Kumar, Ashok Kumar, Rahul Shukla, Anshita Chole, Vikrant Moolchandani, Jhankar Amity School of Engineering and Technology Amity University Department of Computer Science and Engineering Madhya Pradesh Gwalior India

ISBN: (纸本)9798350387490

This paper aims to predict student academic performance using machine learning algorithms by analyzing socioeconomic and personal factors from secondary education in Portugal. The study utilizes a dataset containing demographics, family background, educational support, extra-curricular activities, and academic records to evaluate their impact on students' success in Mathematics and Portuguese courses. Machine learning models such as Logistic Regression, Decision Trees, and Random Forests were employed to analyze the data. The Random Forest model outperformed others with an accuracy of 85 %, indicating significant potential in predicting student performance using the selected features. © 2024 IEEE.

关键词： Adversarial machine learning

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A Review of Deep Convolutional Neural Networks in Mobile Face Recognition

International Journal of Interactive Mobile Technologies

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International Journal of Interactive Mobile Technologies 2023年第23期17卷 4-19页

作者： Chi, Jing On, Chin Kim Zhang, Haopeng Chai, Soo See Faculty of Computing and Informatics University Malaysia Sabah Sabah Malaysia School Information and Electrical Engineering Hebei University of Engineering Hebei China Faculty of Computer Science and Information Technology Universiti Malaysia Sarawak Sarawak Malaysia

With the emergence of deep learning, Convolutional Neural Network (CNN) models have been proposed to advance the progress of various applications, including face recognition, object detection, pattern recognition, and number plate recognition. The utilization of CNNs in these areas has considerably improved security and surveillance capabilities by providing automated recognition solutions, such as traffic surveillance, access control devices, biometric security systems, and attendance systems. However, there is still room for improvement in this field. This paper discusses several classic CNN models, such as LeNet-5, AlexNet, VGGNet, GoogLeNet, and ResNet, as well as lightweight models for mobile-based applications, such as MobileNet, ShuffleNet, and EfficientNet. Additionally, deep CNN-based face recognition models, such as DeepFace, DeepID, FaceNet, and SphereFace, are explored, along with their architectural characteristics, advantages, disadvantages, and recognition accuracy. The results indicate that many scholars are researching lightweight face recognition, but applying it to mobile devices is impractical due to high computational costs. Furthermore, noise label learning is not robust in actual scenarios, and unlabeled face learning is expensive in manual labeling. Finally, this paper concludes with a discussion of the current problems faced by face recognition technology and its potential future directions for development. © 2023 by the authors of this article. Published under CC-BY. All Rights Reserved.

关键词： Neural network models

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Eye Disease Detection Using CNN, ResNet, and VGG16 3rd

Eye Disease Detection Using CNN, ResNet, and VGG16

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3rd International Conference on Universal Threats in Expert Applications and Solutions, UNI-TEAS 2024

作者： Bano, Sofiya Agrawal, Bhawana Bhardwaj, Shweta Department of Computer Science and Engineering Amity School of Engineering and Technology Amity University Uttar Pradesh Noida India

ISBN: (纸本)9789819751457

This chapter deals with the application of deep learning models, specifically convolutional neural networks (CNNs), ResNet, VGG16, and VGG19, in the domain of eye disease detection. Early and accurate diagnosis of eye conditions is critical for preserving vision and preventing irreversible damage. Our research involves collecting and preprocessing ophthalmic images, customizing and training deep learning models, and rigorously evaluating their performance across a diverse dataset. We also provide a practical case study demonstrating the real-world application of these AI models in clinical contexts, showcasing their potential to expedite diagnosis and support healthcare professionals. The report concludes with insights into future research directions, recognizing the evolving landscape of AI in healthcare and its promising role in improving eye disease detection. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Convolutional neural networks

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AwGhO-Res-Trans: Adaptive Weighting Grasshopper Optimization-Based Residual Transformer for Autism Detection 4

AwGhO-Res-Trans: Adaptive Weighting Grasshopper Optimization...

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4th International Conference on Sustainable Expert Systems, ICSES 2024

作者： Balasubramani, Jagadesh Surendran, R. Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798331540364

In this model, input data is acquired from publicly available datasets and converted into a normalized form using the Min-Max normalization approach. The Pre-processing based on normalization is employed to scale the data within a specific range to reduce complexity. Following normalization, the most relevant features are selected using the Wrapper Feature Selection Model, which iteratively evaluates and selects appropriate features that enhance Autism detection accuracy. Finally, Autism detection is devised using the proposed Adaptive Weighting Grasshopper Optimization Residual Transformer (AwGhO-Res-Trans) model. The designed model demonstrated enhanced outcomes compared to the baseline models in Autism detection. © 2024 IEEE.

关键词： Diseases

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