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Forecasting Continuous Air Pollution with Cloud-Based Big Data Platform using Spatial-Temporal Visualization

Forecasting Continuous Air Pollution with Cloud-Based Big Da...

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International Conference for Convergence of technology (I2CT)

作者： Yue Shan Chang Satheesh Abimananan Saurabh Shukla Dhivyadharsini Satheesh Department of Computer Science and Information Engineering National Taipei University New Taipei City Taiwan Department of Computer Science and Engineering Amity University Maharashtra Mumbai India School of Computer Science and Engineering Vellore Institute of Technology Vellore India

In today’s era of rapidly growing data, having a well-designed platform for analyzing and visualizing big data is becoming increasingly important. This study presents a cloud-based big data platform for analyzing and visualizing continuous spatial-temporal air pollution. The platform combines a Spark Hadoop-based machine learning environment with a TensorFlow-based deep learning framework for forecasting and a map for demonstration and visualization of the results. The platform uses the Inverse Distance Weighted (IDW) method to estimate the concentration of particulate matter in the atmosphere and a cloud based non-SQL database to store the pre-calculated air quality images. The platform also features a sliding window mechanism, which allows users to easily visualize the results over time. This work provides an instant overview of air quality in Taiwan and evaluates the performance of the platform, concluding that it is efficient and capable of providing continuous spatial-temporal results. This platform represents a significant step forward in the field of air quality monitoring and forecasting.

关键词： Deep learning Databases Clouds Data visualization Big Data Air pollution Sparks

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Enhancing Autonomous Vehicle Navigation with Real-Time Object Detection Using Convolutional Neural Networks

Enhancing Autonomous Vehicle Navigation with Real-Time Objec...

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International Conference on Contemporary Computing and Informatics (IC3I)

作者： Arnav Kotiyal Zaid Alsalami Nandini Shirish Boob Arti Badhoutiya T Mounika Muthuswamy Jayanthi Prateek Chaturvedi Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun Department of computers Techniques engineering College of technical engineering The Islamic university Najaf Iraq Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai Tamil Nadu Department of Electrical Engineering GLA University Mathura Department of Computer Science and Engineering MLR Institute of Technology Hyderabad Telangana Department of Electronics and Communication Engineering New Horizon College of Engineering Bangalore Amity School of Engineering & Technology Amity University Greater Noida Uttar Pradesh India

ISBN: (数字)9798350350067

ISBN: (纸本)9798350350074

Autonomous vehicles’ (AVs) capacity to identify and categorise things in real-time is crucial to their development for the sake of efficient and safe navigation. Because of its exceptional capability to learn spatial feature hierarchies automatically, Convolutional Neural Networks (CNNs) have become an indispensable tool for object identification in intricate visual recognition problems. This research offers a thorough analysis of convolutional neural networks (CNNs) used for autonomous vehicle object identification in real-time, with an emphasis on improving detection accuracy and speed. We compare the processing speed, detection accuracy, and computational efficiency of several CNN designs, such as YOLO (You Only Look Once), SSD (Single Shot Multibox Detector), and Faster R-CNN. In addition, to address the demanding realtime needs of autonomous driving systems, we suggest CNN design improvements that use hardware acceleration methods like GPU and TPU optimisations. The experimental findings show that our suggested method significantly reduces detection latency while maintaining accuracy, which makes it suitable for use in real-world AV settings. Autonomous vehicle technology advances thanks to this study’s results, which aid in building scalable and reliable object identification algorithms.

关键词： YOLO Accuracy Navigation Graphics processing units Real-time systems Convolutional neural networks Object recognition Autonomous vehicles Optimization Hardware acceleration

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Next-Generation Computational Models for Enhanced Interpretation of Biospeckle Images Leveraging Deep Learning and Fourier Transform Techniques in Biomedical Applications

Next-Generation Computational Models for Enhanced Interpreta...

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International Conference on Contemporary Computing and Informatics (IC3I)

作者： R Gowtham Raj S Sreekanth Ginni Nijhawan Shubhi Gupta Saurabh Sharma T Mounika Deparment of Mechanical Engineering New Horizon College of Engineering Bangalore India Computer Science and Engineering(Data Science) Institute of Aeronautical Engineering Dundigal Hyderabad India Plot No. 11 Knowledge Park II Greater Lloyd Law College Noida Uttar Pradesh India Lovely Professional University Phagwara India Amity School of Engineering & Technology Amity University Greater Noida India Department of Computer Science and Engineering MLR Institute of Technology Hyderabad Telangana India

ISBN: (数字)9798350350067

ISBN: (纸本)9798350350074

This study looks at advanced computational techniques that enhance the understanding of biospeckle images in biological contexts. Our models use Fourier transform and deep learning techniques to achieve improved specificity, recall, accuracy, and precision. The Fourier transform may provide a more accurate description of frequency domain data as biological processes evolve over time. Deep learning algorithms recognise patterns in biospeckle images, facilitating analysis, memory, and response. The analysis research delineates the several components that influence the outcome and provides evidence of their respective roles. This research shows how helpful the models are and offers suggestions on how to make them better. Non-invasive biomedical testing uses these computer models to illustrate the temporal evolution of biological cells. Our techniques improve the interpretation of biospeckle images, potentially revolutionising non-invasive biomedical imaging. Future researchers may evaluate these models using different datasets and testing conditions. This guarantees their adaptability and power in biological contexts.

关键词： Deep learning Adaptation models Fourier transforms Accuracy Evolution (biology) Computational modeling Biological system modeling Frequency-domain analysis Biomedical imaging Testing

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Edge Intelligence Hybrid Framework for Real-Time IoT Applications Using Multi-Stage Adaptive Processing Methods

Edge Intelligence Hybrid Framework for Real-Time IoT Applica...

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Mobile Computing and Sustainable Informatics (ICMCSI), International Conference on

作者： Sajja Suneel Rakesh Kumar Yadav K.Nirmala Devi B.Teja Sree Aarthi R Shrikant Upadhyay Department of CSE(Data Science) Institute of Aeronautical Engineering Hyderabad Telangana India Amity School of Engineering and Technology Amity University Patna Bihar Department of Computer Applications Madanapalle Institute of Technology & Science Department of IT S.R.K.R Engineering College Bhimavaram Department of ECE Rajalakshmi Institute of Technology Chembarambakkam Tamil Nadu Department of Electronics and Communication Engineering MLR Institute of Technology Hyderabad India

ISBN: (数字)9798331522667

ISBN: (纸本)9798331522674

The rapid proliferation of IoT devices has created an unprecedented need for real-time data processing and decision-making. This paper proposes an Edge Intelligence Hybrid Framework designed for real-time IoT applications using multi-stage adaptive processing methods. The framework structured into three layers: edge-level processing, fog-level aggregation, and cloud-level computation, enabling efficient distribution of tasks across the hierarchy. At the edge, lightweight models and preprocessing techniques, such as Kalman Filtering and Huffman Coding, ensure low-latency and energy-efficient operation. Intermediate computations and model synchronization occur at the fog level, while high-complexity tasks like deep learning model training performed at the cloud level. Results demonstrate that edge-level processing achieves an accuracy of 85.4 %, while fog and cloud levels improve accuracy to 89.4% and 94.9%, respectively. Latency at the edge is as low as 9-13ms, compared to fog (23-29ms) and cloud (117-125ms), ensuring suitability for time-critical IoT tasks. The proposed framework effectively balances latency, accuracy, and resource optimization, making it ideal for applications such as predictive maintenance, healthcare, and smart surveillance.

关键词： Performance evaluation Adaptation models Accuracy Computational modeling Decision making Medical services Real-time systems Internet of Things Time factors Low latency communication

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COVIDetect-DESVM: Explainable framework using Differential Evolution Algorithm with SVM classifier for the diagnosis of COVID-19 4

COVIDetect-DESVM: Explainable framework using Differential E...

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4th International Conference on Recent Developments in Control, Automation and Power engineering, RDCAPE 2021

作者： Dixit, Abhishek Mani, Ashish Bansal, Rohit Amity University Amity School Of Engineering And Technology Department Of Computer Science Noida India Amity University Amity School Of Engineering And Technology Department Of EEE Noida India Rajiv Gandhi Institute Of Petroleum Technology Department Of Management Studies U.P. Rae Bareli India

ISBN: (数字)9781665414296

ISBN: (纸本)9781665414296

The SARS-CoV-2 (COVID-19) epidemic has a huge impact on the health and daily life of people. More than 200 countries are impacted due to this pandemic. To light the COVID-19 virus we need a powerful monitoring system to identify the patients and isolate them. The current detection tests are either done by measuring the body temperature or spotting the genetic material of the SARS-CoV-2. These techniques are time-consuming and have a poor detection rate. Radiological images like chest X-rays are also highlighted and help in the diagnosis of COVID-19 patients. Initial studies suggest that COVID-19 patients have abnormalities in their chest X-rays and can be used in the diagnosis of COVID-19. Based on this literature research, various solutions have been proposed utilizing chest X-rays to detect the SARS-CoV-2. Most of these solutions use non-public datasets and complicated structures with fewer accurate results. In our study, we propose a self-learning, interpretable model for real-time detection of COVID-19. This model utilizes a Differential evolution algorithm for feature selection and Support Vector Machine (SVM) as a classifier. The aim is to obtain higher accuracy in detecting COVID-19 infected patients using X-ray images. We have also used the LIME explanation algorithm to explain the predictability of our model and this makes our design very robust and sustainable. This fully transparent, Interpretable, and explainable model can be used in hospitals where there is a huge demand for rapid tests and radiologists are busy. © 2021 IEEE.

关键词： Coronavirus

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Enhancing Microgrid Stability: Optimal BESS Sizing via Modified PSO for Frequency Control and Cost Efficiency

Enhancing Microgrid Stability: Optimal BESS Sizing via Modif...

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International Conference on Contemporary Computing and Informatics (IC3I)

作者： S. Vikram Singh V Revathi B Sankara Babu Munugapati Bhavana Navdeep Singh G Nagamani Amity School of Engineeringh and Technology Amity University Greater Noida Uttar Pradesgh India Department of Applied Sciences New Horizon College of Engineering Bangalore Department of Computer Science and Engineering GRIET Bachupally Hyderabad India Department of Computer Science and Engineering MLR Institute of Technology Hyderabad Telangana India Lovely Professional University Phagwara India Institute of Aeronautical Engineering Hyderabad Telangana India

ISBN: (数字)9798350350067

ISBN: (纸本)9798350350074

When constructing a Battery Energy Storage System (BESS) to be incorporated in a microgrid, the main objective is to minimise the risk of microgrid stability and system failure. An improper or careless sizing of the BESS may lead to system losses, excessive capacity, and high installation costs. Consequently, this study suggest a novel technique for managing the frequency of the independent microgrid using an improved particle swarm optimisation (PSO), which aims to find the optimum size of the BESS while minimising its total cost. Our research focuses on finding the most cost-effective way to deploy BESS in microgrids for a period of 15 years. We will also recommend the optimal measurements of BESS to minimise microgrid volatility and system failure in the case of utility grid outage. We will use the PSO approach for frequency regulation. The next step is to examine a standard microgrid and evaluate BESS in comparison to other existing storage systems in terms of their economic performance. By comparing the optimum size of BESS- modified PSO method to both the conventional size of BESS and the ideal size of BESS-based empirical approach, the findings show that the latter may improve the dynamic system effectiveness. In a typical microgrid, the polysulfide-chromium BESS is predicted to be cheaper throughout the 15 years of deployment compared to the chrome redox BESS, when evaluating the financial health of the two storage techniques. Findings indicate that microgrid planning flexibility, grid security, and power system stability may all be greatly enhanced using the suggested PSO method-based frequency management. Simultaneously, it is able to meet the needs for frequency control economically.

关键词： Costs Power system dynamics Microgrids Power system stability Redox Stability analysis Planning Security Frequency control Standards

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Vehicle Number Plate Detection: An Edge Image Based Approach 4th

Vehicle Number Plate Detection: An Edge Image Based Approach

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4th International Conference on Advanced Computing and Intelligent engineering, ICACIE 2019

作者： Jena, Kalyan Kumar Nayak, Soumya Ranjan Mishra, Sasmita Mishra, Sarojananda Utkal University Bhubaneswar India Amity School of Engineering and Technology Amity University Noida NoidaUttar Pradesh India Department of Computer Science Engineering and Applications Indira Gandhi Institute of Technology Sarang India Department of Computer Science and Engineering Parala Maharaja Engineering College Berhampur India

ISBN: (纸本)9789811565830

Transportation system (vehicle communication) plays a major role in today’s scenario. Detection of vehicle number plate exactly in blurry conditions was the most challenging issue found in the last three decades. Although many intensive studies were undertaken, none addressed this problem exhaustively. Various methods are introduced by several researchers for detecting the vehicle number from the vehicle number plate images. The purpose of this study was to investigate this current issue by implementing an edge-based approach on the basis of quantitative combination of Canny, Morphological and Sobel methods for the accurate detection of vehicle number in blurry conditions. The experimental results demonstrated that the proposed scheme outperforms its counterparts in terms of Sobel, Prewitt, Roberts, Laplacian of Gaussian (LoG), Morphological and Canny methods in all aspects with higher peak signal to noise ratio (PSNR) and signal to noise ratio (SNR) values. Hence, the proposed hybrid scheme is better and robust and results in accurate estimation of vehicle number from the blurry vehicle number plate (BVNP) images for the given datasets. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Signal to noise ratio

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Leveraging UAV technology for Enhanced Disaster Management: Innovations and Applications

Leveraging UAV Technology for Enhanced Disaster Management: ...

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2024 IEEE International Conference on Signal Processing and Advance Research in Computing, SPARC 2024

作者： Upadhyay, Govind Murari Vats, Prashant Shanker, Surabhi Bhushan, Saloni Mittal, Sonu Mandot, Manju Manipal University Jaipur Dept. of Computer Application Rajasthan Jaipur India Manipal University Jaipur School of CSE Dept. of CSE Rajasthan Jaipur India K.R. Mangalam University School of Engineering & Technology Dept. of CSE Sohna Road Haryana Gurugram India Chhatrapati Shivaji Maharaj University Dept. of Computer Science/IT Maharashtra Panvel India Amity Institute of Information Technology Amity University Noida Dept. of CSE Uttar Pradesh India J. R. N. Rajasthan Vidyapeeth University Dept. of Computer Science/IT Rajasthan Udaipur India

ISBN: (纸本)9798350385199

Unmanned Aerial Vehicles (UAVs) have substantially advanced the sector by providing improved real-time monitoring, data collecting, and situational awareness capabilities when integrated into disaster management techniques. This study examines the creative uses of unmanned aerial vehicles (UAVs) in disaster relief, emphasising how they may speed up reaction times and increase operational effectiveness in emergency situations. With an emphasis on their usefulness in human detection, damage assessment, and coordination efforts, we propose a thorough framework that describes the design, deployment, and evaluation of UAV systems customised for catastrophe situations. The study shows how UAVs may be used to enhance processes of decision-making and enable more efficient recovery and catastrophe response through instances and quantitative evaluation. The results demonstrate how UAV technology may revolutionise conventional disaster management procedures and provide insightful information for future studies and real-world applications in the field. © 2024 IEEE.

关键词： Disaster prevention

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Detecting AI-Generated Speech Manipulation through CNN-BiLSTM Hybrid Networks

Detecting AI-Generated Speech Manipulation through CNN-BiLST...

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IEEE International Conference on Signal Processing, Computing and Control (ISPCC)

作者： Rahul Dixit Arth Agrawal Anuja Dixit Sweta Tripathi Department of Artificial Intelligence Sardar Vallabhbhai National Institute of Technology Surat Surat India Department of Computer Science Vellore Institute of Technology Vellore Vellore India Department of Computer Science Motilal Nehru National Institute of Technology Allahabad Prayagraj Prayagraj India Department of ECE Amity School of Engineering and Technology Amity University Haryana

ISBN: (数字)9798331538934

ISBN: (纸本)9798331538941

Audio forgery, the malicious manipulation or generation of audio content, poses significant threats to privacy, security, and digital trust. Various audio forgeries are prevalent today, including copy-move forgery, splicing, noise injection, Text-to-Speech (TTS), time stretching, pitch shifting, voice conversion (VC), resampling, and deep forgery. Among these, TTS and VC attacks emerge as particularly concerning due to their ability to mimic genuine voices, enabling identity theft and misinformation convincingly. While existing research predominantly focuses on detecting copy-move forgeries, this paper addresses the relatively underexplored challenges of detecting Text-to-Speech and voice conversion forgeries. A hybrid CNN-Bidirectional LSTM model is proposed, which uses convolutional layers for spectral feature extraction and bidirectional LSTM layers for temporal pattern recognition. To enhance robustness, audio augmentations are applied during preprocessing. The model is built and tested on the ASVspoof 2019 LA dataset, achieving an accuracy of 96.65% in identifying advanced audio forgeries. This study bridges a critical gap in audio forgery detection and provides a foundation for safeguarding audio authenticity against emerging threats.

关键词： Accuracy Splicing Bidirectional long short term memory Feature extraction Forgery Robustness Hybrid power systems Text to speech Pattern recognition Security

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Colon Cancer Tissue Classification Using ML

Colon Cancer Tissue Classification Using ML

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International Conference on Information Systems and computer Networks (ISCON)

作者： Ashish Tripathi Kuldeep Kumar Anuradha Misra Brijesh Kumar Chaurasia Department of Computer Science and Engineering Amity School of Engineering & Technology Amity University Lucknow Lucknow Uttar Pradesh India Center for Data Analytics Bond Business School Bond University Gold Coast Queensland Australia Department of Computer Science and Engineering Pranveer Singh Institute of Technology Kanpur Uttar Pradesh India

In this paper, the classification of colon cancer tissues by means of machine learning approaches is evaluated. In today’s world, a revolutionary advancement has come in the classification and diagnosis of diseases in the medical and healthcare sectors. Deep learning classifiers and machine learning methods are now broadly applied to accurately diagnose a number of diseases. Cancer is one of the world’s most significant roots of death, appealing to the lives of one person out of every six. As per the national library of medicine, the third leading cause of death worldwide is colorectal cancer. Identifying an illness at a premature stage increases the chances of survival. Automated diagnosis and the classification of tissues from images can be completed much more quickly with the use of artificial intelligence. A publicly available IoT dataset CRC–VAL–HE–7K consisting of 7180 images, distributed among nine types of colorectal tissues: background, lymphocytes, adipose, mucus, colorectal adenocarcinoma epithelium, normal colon mucosa, debris, cancer-associated stroma, and, smooth muscle is used after preprocessing. Feature extraction is done by applying Differential-Box-Count on all blocks of images. The dataset is evaluated by these Machine Learning (ML) procedures: K-Nearest Neighbor, Support Vector Machine, Decision Tree, Random Forest, Extreme Gradient Boosting, and Gaussian Naive Bayes. Results show that the performance of Extreme Gradient Boosting is the best and most viable approach.

关键词： Deep learning Support vector machines Machine learning algorithms Transfer learning Feature extraction Boosting Colon

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