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Smart City Surveillance: Survey on Current Status and Future Trends 14

Smart City Surveillance: Survey on Current Status and Future...

引用

14th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2023

作者： Neyaz, Hera Paul, Hrituparna Srivastava, Anima Singh, Shiksha Dept. of Computer Science and Engineering Shambhunath Institute of Engineering and Technology India

This paper presents a comprehensive survey on surveillance and security aspects of a smart city. Multiple dimensions of a smart city surveillance system have been discussed. The current trends and methodologies are thoroughly researched and presented. The algorithms of novel methods are reviewed along with analytics and methodologies. Impact of XAI in the smart city surveillance is discussed. Advantages and disadvantages of current infrastructures have been included in the broad literature survey. Lastly, the challenges and future trends are conferred to. © Grenze Scientific Society, 2023.

关键词： Smart city

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Automated Hematological Analysis:Advances in Segmentation Approaches and Future Directions 6

Automated Hematological Analysis:Advances in Segmentation Ap...

引用

6th International Conference on Robotics and Automation in Industry, ICRAI 2024

作者： Eram, Saleha Akram, Muhammad Usman Nayab, Maham National University of Sciences and Technology Dept. Computer and Software Engineering Islamabad Pakistan

ISBN: (纸本)9798350355703

Hematological analysis is crucial for diagnosing blood disorders such as leukemia and anemia, a process that has traditionally depended on manual microscopy, and is susceptible to variability and human error. The quest for more reliable and efficient diagnostic methods has led to the development of automated systems. This review examines the methods used for automated blood cell segmentation, with a particular emphasis on deep learning approaches such as semantic and instance segmentation. Advanced models like Mask R-CNN and DeepLabv3+ were evaluated for their effectiveness in accurately segmenting and identifying blood components in complex histological images. The findings revealed that Mask R-CNN achieved a segmentation accuracy of 92%, while DeepLabv3+ came in close behind at 94.5%. Both models significantly surpassed traditional methods, which generally provide accuracy rates of 75-80 %. Despite these advancements, the review highlights ongoing challenges related to the robustness and scalability of these techniques in clinical settings. The paper concludes by emphasizing the transformative potential of deep learning in hematological diagnostics and calls for further research to address current limitations and improve patient outcomes. © 2024 IEEE.

关键词： Semantic Segmentation

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Real Estate Management System using Blockchain

Real Estate Management System using Blockchain

引用

2023 International Conference on Innovations in engineering and technology, ICIET 2023

作者： Peter, Aaron Kumar, Aromal Anil Rajeev, Ashwin Baiju, Ben Chooralil, Varghese S. Rajagiri School of Engineering and Technology Dept of Computer Science and Engineering Kochi India

ISBN: (纸本)9798350309980

Real Estate is the third largest asset class in the world only behind the stock market and the bond market. The entire real estate market is valued at over $7 trillion and is expected to cross $8 trillion by 2028. With an ever growing population and the land being limited in the world it would be safe to assume that this asset class keeps on growing according to the simple rules of law of Supply and Demand. So we have to come up with new methods to improve the process of purchasing real estate as well as eliminate the third parties in between. So in order to make these processes more efficient we can use the technology of blockchain in the process. We can improve the various issues involving transparency and also promote partial ownership of properties. © 2023 IEEE.

关键词： Blockchain

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Predicting Recurrence in Differentiated Thyroid Cancer: A Machine Learning Approach

Predicting Recurrence in Differentiated Thyroid Cancer: A Ma...

引用

2024 International Conference on Advances in Data engineering and Intelligent Computing Systems, ADICS 2024

作者： Bharath, K. Sai Sabitha, A. Hindustan Institute Of Technology And Science Dept Of Computer Science And Engineering Chennai603103 India

ISBN: (纸本)9798350364828

A predictive model refers to a mathematical or computational model that is designed to make predictions or forecasts based on input data. These models are used in various fields such as finance, healthcare, marketing, and many others to analyze historical data and make predictions about future events or trends. This study focuses on developing a predictive model for well-differentiated thyroid cancer recurrence using machine learning algorithms. Leveraging a dataset with clinicopathologic features spanning a 15-year period, the study aims to contribute to the advancement of predictive modeling in thyroid cancer prognosis. The Various algorithms, including logistic regression, decision tree, random forest, support vector machine, k-nearest neighbors, and XGBoost, are evaluated for their performance in binary classification. The highest-performing model is then utilized to predict thyroid cancer recurrence in new data. Integrating this with frontend tools has resulted in an intuitive interface that streamlines the process of uploading datasets, executing the predictive model, and presenting the results. This integration has made the prediction process more accessible and user-friendly, ultimately benefiting clinicians and researchers in the field of thyroid cancer research. © 2024 IEEE.

关键词： Forecasting

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Dual Auction Peer-to-Peer Control Scheme for Empowering Prosumers Participation in the Local Energy Markets 4

Dual Auction Peer-to-Peer Control Scheme for Empowering Pros...

引用

4th International Conference on Computing, Mathematics and engineering Technologies, iCoMET 2023

作者： Ikram, Muhammad Ahmed, Salman Khan Marwat, Safdar Nawaz University of Engineering and Technology Dept. of Computer Systems Engineering Peshawar Pakistan

ISBN: (纸本)9798350335316

The emerging trend of distributed and renewable energy sources encourages energy trading in the electricity market. Various forms of energy markets are evolving for successful energy trading. The local energy market is one of them in which various participants are actively coordinating for optimal utilization of power. Such type of local energy markets provides individual prosumers to trade their surplus energy in the neighbourhood in peer-to-peer topology. The complexity of such type of local energy market is increasing in terms of structure, control, coordination, and cost estimation. For the cooperative behaviour of the local energy markets, a dual auction coordination control scheme is important to encourage more prosumers for energy trading. We proposed a novel scheme that performs dual auctions for prosumers in a decentralized fashion. We assessed dual auction matching by coordinating prosumers and consumers to sell and purchase energy in the local energy market. The aggregator performed balancing in the proposed dual auction scheme as an autonomous entity. The results were simulated in MATLAB and JADE for the complex prosumer peer-to-peer network. Our proposed scheme shows each peer dual auction matching improves from 0.95 to 2.03 for optimal hierarchal energy trading in energy markets. For peer participation, the participation ratio increased from 0.684 to 1.74 and optimal aggregator coordination from 9.18% to 19% in the local energy markets. © 2023 IEEE.

关键词： Smart power grids

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User Specific Real-Time Noise Cancellation using Deep Learning 15

User Specific Real-Time Noise Cancellation using Deep Learni...

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

作者： Kadu, Anil Argade, Advay Deore, Aditya Anand, Asit Nehete, Atharva Alurkar, Arya Dept. of Instrumentation Engineering Vishwakarma Institute of Technology Maharashtra Pune India Dept. of Computer Engineering Vishwakarma Institute of Technology Maharashtra Pune India

ISBN: (纸本)9798350370249

Noise cancellation is a critical concern in various domains, from personal audio experiences to professional settings. This paper presents a novel approach utilizing machine learning, specifically Convolutional Neural Networks (CNNs) trained on urban sound datasets and Yet Another Multilayered Network (YAMNet), to identify and cancel real-time noise. Note that the system is designed as an anti-mechanism for rather repetitive sounds and not for those which vary unpredictably. The methodology involves converting sound inputs into 2D arrays and extracting Mel-Frequency Cepstral Coefficients (MFCC) features, conducting pattern recognition, and employing inversion techniques for noise cancellation. The results exhibit promising noise reduction, showcased through visual representations of input and output sound waves. The potential implications and prospects of this approach are also discussed. © 2024 IEEE.

关键词： Convolutional neural networks

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Skin Lesion Classification using Deep Learning Architectures 2

Skin Lesion Classification using Deep Learning Architectures

引用

2nd IEEE International Conference on Networks, Multimedia and Information technology, NMITCON 2024

作者： Gothivrekar, Siddhi Khanvilkar, Anuj Nandi, Utsab Singh, Kamal Maktum, Tabassum Ramrao Adik Institute of Technology Dept. of Computer Engineering Navi Mumbai India

ISBN: (纸本)9798350372892

Skin cancer, originating from diverse dermatological conditions, can be classified based on texture, structure, and color. Identifying lesions from images aids in pre-diagnosis;expanding beyond melanoma, early detection of lesions like Actinic Keratosis and basal cell carcinoma is crucial for timely intervention. Recently, deep convolutional neural networks (DCNNs) have shown promising results in dermatological analysis. This paper presents a comparative study of various DCNNs for the categorization of skin lesions. The proposed method involves fine-tuning the popular architectures such as the VGG16, Inception V3, Inception ResNet V2, and DenseNet 201 trained using the HAM10000 dataset. The paper investigates fine-tuning just the top layers and all the layers of the models, aiming to identify the most effective approach. According to the work's experimental findings, fine-tuning the complete trained model performs significantly better. Moreover, the study finds that fewer epochs and less computational time are required for fine-tuning the entire model while achieving higher accuracy. © 2024 IEEE.

关键词： Convolutional neural networks

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Detection of Cataracts from Retinal Fundus Image Using Ensemble Machine Learning Approach

Detection of Cataracts from Retinal Fundus Image Using Ensem...

引用

2023 International Conference on Next-Generation Computing, IoT and Machine Learning, NCIM 2023

作者： Hasan, Md. Nahid Hossain, Md. Ali Rajshahi University of Engineering and Technology Dept. of Computer Science and Engineering Bangladesh

ISBN: (纸本)9798350316001

Cataract is a common eye condition that causes clouding of the eye's natural lens, resulting in blurry vision and it is very common in older ages people. However, it can also occur in younger aged people due to various genetical effects, diabetes, smoking, and prolonged exposure to sunlight. Cataracts can be treated through surgery to remove the clouded lens and replaced by an artificial one, which can significantly improve the vision. This problem can be reduced through early detection, regular eye examinations etc. In this research, a Contrast Limited Histogram Equalization (CLAHE) of retinal fundus images is applied for a better presentation of the above cataract effects. These improved images are fed to a machine learning-based model for effective detection. Finally, the ensembling of the proposed machine learning-based classifiers is performed for the accurate detection of cataracts. The proposed model was tested experimentally on a real dataset and achieves excellent performance with accuracy, precision, and recall scores of 99.67%, 100%, and 99.25%, respectively which outperform the baseline methods. The robustness of the proposed ensemble model is evaluated through 5-fold cross-validation and achieved an average accuracy of 99.6%. The implementation code can be accessed from the GitHub repository: https://***/nahid-tech/cataract-detection. © 2023 IEEE.

关键词： Image enhancement

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A Novel Approach for Single-Shot Target Recognition by Matching Graph of Feature Pixels 2

A Novel Approach for Single-Shot Target Recognition by Match...

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2nd International Conference on Machine Learning and Data engineering, ICMLDE 2023

作者： Mitra, Baldivya Deshmukh, Maroti Kumar, Abhimanyu Dept. of Computer Science & Engineering National Institute of Technology Uttarakhand 246174 India

Image feature extraction and matching constitute fundamental steps in computer vision and image analysis, enabling various applications ranging from object recognition to scene reconstruction. Object recognition is one of the major applications of artificial intelligence. Since last two decades, different techniques for object recognition were given by researchers from across the world. This paper presents a novel approach for automatic target recognition in autonomous weapons by matching connected graph of feature pixels. The Moving and Stationary Target Acquisition and Recognition (MSTAR) is taken for experimental purpose. The Proposed model has three stages for a target recognition;Image Denoising, Feature extraction & graph generation, and feature matching. Firstly, a sample image of targets taken from the MSTAR is taken as an input. Further, input sample is de-noised and features are extracted using Speeded-Up Robust Features (SURF) algorithm. Thereafter, a graph is created using feature pixels and this complete process is repeated with test image. Further, both graphs are compared using a proposed feature graph matching algorithm which results an recognition accuracy score. Proposed model is tested over the MSTAR dataset and it resulted in varying accuracy at different feature matching thresholds. © 2024 Elsevier B.V.. All rights reserved.

关键词： Synthetic aperture radar

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Flood Location, Management and Solution(FLMS): A Flood Prediction and Management System for Kurla 3

Flood Location, Management and Solution(FLMS): A Flood Predi...

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3rd International Conference on Advanced Computing Technologies and Applications, ICACTA 2023

作者： Naidu, Siddhanth Pandey, Ambuj Patel, Shiab Wagaskar, Kalpita Tripathy, Amiya Kumar Sen, Sumit Don Bosco Institute of Technology Dept. of Computer Engineering Mumbai India Indian Institute of Technology Bombay Dept. of Computer Engineering Mumbai India

ISBN: (纸本)9798350348347

Flooding is a common problem in Kurla, a suburban area of Mumbai, India, with significant impacts on infrastructure, public health, and their livelihoods. This paper proposes FLMS, a Flood Location, Management, and Solution system designed to predict flood events in Kurla to assist in managing them. The FLMS system uses historical data on rainfall gathered from different sources, water levels, and other factors to develop a prediction model based on machine learning techniques. FLMS being a web based application will allow its users to view alerts, contact the respective personnel and create awareness about the different stages of a disaster and ways to limit the losses caused by floods. © 2023 IEEE.

关键词： Machine learning

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