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Deep learning based Air Quality Forecasting using Satellite Data 6

Deep Learning based Air Quality Forecasting using Satellite ...

引用

6th international conference on Mobile computing and Sustainable Informatics, ICMCSI 2025

作者： Sree, K. Navya Reddy, Mancharla Rakesh Saraswathi, S. Department of Computer Science and Engineering Specialization with AI & ML Sathyabama Institute of Science and Technology Chennai India Department Computer science and engineering Sathyabama Institute of Science and Technology Chennai India

ISBN: (纸本)9798331522667

Air pollution is a significant threat to human health and the environment. Accurate air quality forecasting is essential for effective mitigation strategies, including public health advisories, emission control measures, and policy interventions. This research focuses on the development of an advanced air quality forecasting system using machine learning techniques. The system uses historical data on pollutants such as PM2.5, NO2, and O3, along with meteorological parameters, to predict future air quality levels. The model is enhanced with the inclusion of sophisticated machine learning algorithms to better capture the intricate spatiotemporal patterns and enhance the accuracy of forecasting. The system may potentially provide real-time air quality forecasts that enable proactive measures for protecting public health and inform decisions for environmental policy. This research contributes to sustainable and resilient urban environments by providing valuable insights into air quality dynamics and supporting effective air pollution mitigation strategies. © 2025 IEEE.

关键词： air pollution Air quality forecasts cardiovascular diseases data-driven strategies environmental policy global climate international collaboration machine learning NO2 O3 PM2.5 policy-making pollution mitigation proactive alerts public health real-time monitoring respiratory diseases spatial variability temporal variability transboundary pollution

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University Buildings as an Opportunity for Urban Regeneration: Sustainable and Innovative teaching and Research Spaces 11th

University Buildings as an Opportunity for Urban Regeneratio...

引用

11th international conference of Ar.Tec.

作者： Greco, Alessandro Morandotti, Marco Besana, Daniela Mericco, Mauro Pelini, Francesca Univ Pavia I-27100 Pavia Italy

ISBN: (纸本)9783031718571;9783031718557;9783031718540

The Italian architectural heritage, in particular that linked to education, learning and research, is rather dated since in some cases schools and universities are set up into historical buildings that have been adapted and reused (mostly former convents and barracks) or new buildings specially created, that largely date back to the last century, according to typology distribution and construction technologies that are difficult suitable with the needs of digital and computerized teaching and with the dynamism of research activities. It follows that spaces for education and research often demonstrate poor functionality compared to current requirements and inadequacy with respect to the needs of both energy-environmental and economic-management sustainability. Fortunately, since 2019 a season of modernization of university structures has opened thanks to two MUR tenders which have made over 1,900 million euros available to universities for redevelopment, modernization and renewal projects of spaces to be allocated to institutional activities. This contribution aims to underline the role that universities can play as drivers of urban regeneration and building redevelopment thanks to the projects that these fundings (absent for too long) will guarantee in the coming years. New buildings for learning and research can be the starting point for a new sustainable future because they can support the establishment of a sustainable culture due to their impact on next generation. Referring to a specific case study (the urban regeneration of the Scientific Institutes area of the University of Pavia) the paper highlights approaches and methods that can also be exported to other similar contexts (in terms of size, functions and organization) through a cognitive analysis, sustainability-oriented planning and programming followed by participatory and multidisciplinary planning, essential to achieve the objectives of quality education (SDG 4) and sustainable cities and communities (

关键词： Building for learning University Urban Regeneration Sustainability Innovation

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Fake Job Post Detection Using Machine learning

Fake Job Post Detection Using Machine Learning

引用

2025 international conference on computing for Sustainability and Intelligent Future, COMP-SIF 2025

作者： Pradeep Kumar, B.P. Bhagya, D.J. Gagana, G. Mani, N. Dept. of Computer Science and Engineering Atria Institute of Technology Karnataka Bangalore India

ISBN: (数字)9798331538538

ISBN: (纸本)9798331538538

By examining patterns in the text data and metadata linked to job adverts, The use of learning algorithms has grown in popularity in the detection of fraudulent job postings. For this, Research Machine emphasises the application of a number of algorithms, such as Naive Bayes and Random Forest, Together with models for deep learning, like CNN and Bidirectional LSTM, and AdaBoost. To extract useful features These techniques depend on text preparation techniques such as lemmatisation, tokenisation, and TF-IDF vectorisation. To differentiate between authentic and fraudulent posts, models are instructed in datasets and evaluated using measures such as accuracy, accuracy, memory, and F1-score. Advanced classifiers and group techniques have proven to be quite successful in enhancing detection performance while lowering false positives and negatives. © 2025 IEEE.

关键词： Random forests

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Sign Language to Text Conversion using Random Forest 6

Sign Language to Text Conversion using Random Forest

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6th international conference on Mobile computing and Sustainable Informatics, ICMCSI 2025

作者： JayaLakshmi, M. Chowdari, Allu Pranathi Gowthami, Andra Deepthika, Akki Department of Computer Science and Engineering Kalasalingam Academy of Research and Institution Anand Nagar Tamilnadu Krishnankoil India

ISBN: (纸本)9798331522667

In this study, a comprehensive approach for translating sign language into text using computer vision, machine learning, and real-time processing techniques is presented. The proposed system uses OpenCV for capturing the images and frames and Media Pipe is utilized for tracking hand movements and detection of landmarks. The landmarks extracted are processed and normalized then they are used as features for a Random Forest Classifier which is a machine learning algorithm well known for its classification accuracy. The process begins with capturing the images using the camera then the images are organizes into different categories representing numerous signs. By these images the hand landmarks are extracted and stored using Pickle for training process that happen later and data is being *** real-time scenario, the system collect the images, detect hand landmarks and classifies the images based on the *** research provide communication accessibility for the deaf and disabled community. © 2025 IEEE.

关键词： Adversarial machine learning

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Deep learning for Solar Panel Fault Detection: Integrating GAN and ResNet Models 6

Deep Learning for Solar Panel Fault Detection: Integrating G...

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6th international conference on Mobile computing and Sustainable Informatics, ICMCSI 2025

作者： Manimegalai, V. Oviya, B. Kargvel, S. Umesh Vilashini, P.L. Mohanapriya, V. Elakya, A. Department of Electrical and Electronics Engineering Sri Krishna College of Technology India Department of Electrical and Electronics Engineering Bannari Amman Institute of Technology India

ISBN: (纸本)9798331522667

The rapid expansion of solar energy implementation has highlighted critical challenges related to temperature variations and environmental factors impacting photovoltaic (PV) systems. This paper addresses the need for continuous monitoring to ensure the efficiency and reliability of solar panels. PV panels frequently encounter operational issues including surface contamination, irregular shading patterns, and thermal irregularities, which can result in hotspot formation, leading to substantial power losses and accelerated panel deterioration. The Machine learning algorithms play a crucial role in detecting defects in solar panels, such as cracks, shading, and hotspots, by analyzing both thermal and visible light images. The implementation of deep learning architectures, specifically ResNet for feature extraction and Generative Adversarial Networks (GANs) for pattern recognition, enables precise real-time fault detection. The demonstrated success of this integrated deep learning approach establishes a new benchmark for PV system monitoring, enhancing both the reliability and longevity of solar installations while minimizing operational downtime. © 2025 IEEE.

关键词： Generative adversarial networks

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A Systematic Literature Review on Dynamic Load Balancing Techniques in Cloud computing Environment: Techniques, Challenges, and Future Prospects 2

A Systematic Literature Review on Dynamic Load Balancing Tec...

引用

2nd international conference on Machine learning and Autonomous Systems, ICMLAS 2025

作者： Divya, B. Dhas, Anto Sahaya APJ Abdul Kalam Technological University Dept. of Computer Science & Engg. Kerala Thiruvananthapuram695016 India Vimal Jyothi Engineering College Chemperi670632 India Vimal Jyothi Engg. College Dept. of Electronics & Communication Engg. Kerala Chemperi670632 India

ISBN: (纸本)9798331505745

Cloud computing is an effective sector that enables institutions and users to buy services based on their requirements. This mechanism provides distinct services including suitable access to web services, platforms for implementation, storage, and so on. In the cloud, load balancing is crucial for achieving optimal usage of resources, minimized response time, and highly advantageous gadget functionality. Cloud providers find it hard to divide the workloads between the servers. Among the distinct complexities, the conventional approaches encounter is achieving equilibrium upon complexities with resilience and the cloud structure's unpredictability. A detailed survey on the hybridization of distinct models with cloud computing for improving the overall system functionality and load balancing is presented. This survey explores various models and their applications in high-performance cloud environments, emphasizing the advantages of combining deep learning techniques with cloud computing resources for load balancing. This review provides an analytical evaluation of current strategies that are used for improving the performance of cloud computing and managing load-balancing issues. Along with deep learning techniques, the optimization approaches used for fine-tuning the network parameters to improve the functionality of cloud computing and manage load-balancing are also explored. Various performance metrics used in the prior works to determine the model's efficacy are reviewed in this work. The survey is concluded with a discussion of the existing research gaps and opportunities for future exploration in the field of deep learning-driven cloud load balancing. © 2025 IEEE.

关键词： Cloud computing Evaluation Measures Load Balancing Research Gaps and Challenges Simulation Environments Techniques Categorization

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An Analysis of Machine learning Algorithms for Predicting Student Performance with a Focus on Teacher and Class Attributes

An Analysis of Machine Learning Algorithms for Predicting St...

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international conference on computing and Machine learning, CML 2024

作者： Gaddi, Alok Kumar, M. Sheetal Kulkarni, Prasad Elemmi, Mahantesh Hiremath, Virupakshi Hiremath, Gurubasvaryya School of Management Studies and Research KLE Technological University Karnataka Hubballi India Justice K S Hegde Institute of Management NMAMIT Karnataka Nitte India Presidency College Karnataka Bengaluru India Navkis College of Engineering Karnataka Hassan India

ISBN: (纸本)9789819778386

The data mining technique (DMT) extracts the core idea and existing knowledge from the available data. Machine learning algorithms are one of the most popular techniques for learning patterns through machines. Only grades and demographic information were used in the current literature review to assess teaching and evaluate a student’s academic success. To evaluate the relationship between students’ academic performance and the faculty's effectiveness as teachers, this study will provide enough discussion about utilizing machine learning algorithms in assessing student performance. The study’s findings show that neural networks are one of the best algorithms for forecasting students’ performance based on instructor and class characteristics. Using machine learning algorithms—Neural Networks in particular—offers significant administrative implications for school administration. These algorithms are a priceless resource for decision support;they help teachers spot children who might perform poorly and schedule interventions promptly. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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Enhanced Onychomycosis Diagnosis Using a Dynamic Weighted Ensemble Classifier: Integrating LightGBM and Transfer learning with a Game Theory Approach 15

Enhanced Onychomycosis Diagnosis Using a Dynamic Weighted En...

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15th IEEE Annual computing and Communication Workshop and conference, CCWC 2025

作者： Idress, Wail M. Zhao, Yuqian Abouda, Khalid A. Ahmed, Aala A Hassan, Wifag Abdalla, Ola Elhindi, Tahani School of Automation Central South University Changsha China Omdurman Islamic University Department of Electrical and Electronic Engineering Omdurman14415 Sudan Alzaiem Alazhari University Electrical Engineering Department Khartoum North13311 Sudan School of Electronic Engineering Sudan University of Science and Technology Khartoum Sudan Khartoum Teaching Hospital of Dermatology and Venereology Khartoum Sudan

ISBN: (纸本)9798331507695

Onychomycosis is one of the most common fungal nail infections for which diagnosis remains difficult, particularly in developing countries like Sudan, as access to specialist dermatologists and diagnostic resources is limited. The traditional diagnostic methods are based on visual inspection and subjective observation that results in misdiagnosis. This paper aims to use a LightGBM classifier for the classification of nail images. In the transfer learning (TL) model, Inception-ResNet-v2, DenseNet121, and EfficientNetB0 are used. A new dynamic weighted ensemble classifier according to the game theory strategy is proposed for the fusion of the TL model and LightGBM classifier model outputs. To overcome the shortage of data availability, a dataset was collected specifically for training the deep learning models with Sudanese patients. The proposed ensemble method outperforms individual deep learning and feature-based models, delivering optimal performance and significantly improving accuracy. This study achieved a diagnostic accuracy of 96.8%, with an AUC of 0.955, sensitivity of 95.45%, and specificity of 91.59%, which surpassed the diagnostic performance of many dermatologists who participated in the study. These results demonstrate the potential of this approach for improving onychomycosis diagnosis in resource-limited settings. © 2025 IEEE.

关键词： Dermatology

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Epilepsy Seizure Detection based on EEG Signals using Machine learning 3

Epilepsy Seizure Detection based on EEG Signals using Machin...

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3rd international conference on Intelligent and Innovative Technologies in computing, Electrical and Electronics, IITCEE 2025

作者： Goel, Aadya School of Computer Science and Engineering Vellore Institute of Technology Vellore632014 India

ISBN: (纸本)9798331515911

Seizure detection is a critical aspect of epilepsy management, and accurate identification can significantly improve patient outcomes. This study explores the application of machine learning techniques, specifically the Random Forest classifier, for the detection of seizures using electroencephalogram (EEG) data. By employing a systematic approach that includes feature extraction and channel reduction, we enhanced the model's performance. Relevant features were extracted from EEG signals, including time-domain statistics and frequency-domain characteristics, which are vital for effective classification. The model was evaluated using standard classification metrics such as precision, recall, F1-score, and accuracy, yielding high performance, particularly in identifying non-seizure events. These findings highlight the potential of ensemble learning methods for real-time seizure monitoring and underscore the importance of this research in developing reliable diagnostic tools. Overall, our results demonstrate that machine learning approaches can significantly contribute to improved patient outcomes in epilepsy management, paving the way for future advancements in clinical applicability. © 2025 IEEE.

关键词： Electroencephalography

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Optimizing Symptom Prediction in Healthcare: An Integrative Machine learning Approach to Regression and Classification Models 3

Optimizing Symptom Prediction in Healthcare: An Integrative ...

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3rd international conference on Intelligent and Innovative Technologies in computing, Electrical and Electronics, IITCEE 2025

作者： Goddilla, Nagarjuna Reddy Sivasangari, A. Sathyabama Institute of Science and Technology Dept of Cse Chennai India

ISBN: (纸本)9798331515911

This research explores the application of combined machine learning techniques for predicting disease symptoms, aiming to support early diagnosis and improve healthcare outcomes. By analyzing both regression and classification models, the study evaluates their respective performance, strengths, and challenges in predicting symptoms. Using a comprehensive dataset with medical records, demographic data, and clinical variables, we developed predictive models to capture the complexities of healthcare data. To enhance accuracy and manage issues like data imbalance and feature diversity, ensemble methods and feature engineering were applied, with the Random Forest classifier achieving a leading accuracy of 91.84%. Additionally, the study emphasizes model interpretability, which is crucial for healthcare professionals to trust and adopt these predictive tools. The findings provide valuable guidance for healthcare professionals and data scientists in choosing machine learning approaches suited to specific medical applications. By bridging the roles of regression and classification models, this research offers a well-rounded perspective on the potential of predictive analytics in healthcare. © 2025 IEEE.

关键词： Adversarial machine learning

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