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5th International Conference on Sustainable Communication Networks and Application, ICSCNA 2024

作者： Nair, Rekha R Babu, Tina Kishore, S. Poornima, J. Sambasivudu, Madduru Bhargavi, Manchikalapudi Alliance School of Advanced Computing Alliance University Department of Computer Science and Engineering Bengaluru India Department of Computer Applications Mangalore India Brindavan College of Engineering Department of Information Science and Engineering Bengaluru India Malla Reddy College of Engineering and Technology Department of Computer Science and Engineering Hyderabad India

ISBN: (纸本)9798331530013

The project develops an OCR system that converts webcam-captured text to speech, featuring user registration for personalized experiences. Key components include image capture via webcam, text extraction using OCR, database storage, and text-to-speech conversion. The system employs Python, OpenCV for image processing, Tesseract for OCR, and Google Text-to-Speech for audio output. Main objectives are to streamline text digitization and assist visually impaired users. The modular design allows for future enhancements like language translation. Comprehensive testing ensures reliability and effectiveness. The system demonstrates an efficient solution for converting printed text to digital and audio formats, with applications in document digitization, education, and accessibility services. It reduces manual effort in text digitization and improves access to written information for visually impaired individuals, showcasing potential across various fields. © 2024 IEEE.

关键词： Optical character recognition

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Optimized Rainfall Prediction Model Using Optune CatBoost: An Innovative Approach 3

Optimized Rainfall Prediction Model Using Optune CatBoost: A...

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3rd Odisha International Conference on Electrical Power engineering, Communication and Computing Technology, ODICON 2024

作者： Babu, Tina Nair, Rekha R Pavithra, K. Alliance School of Advanced Computing Alliance University Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350354379

Rainfall forecasting is important for several uses, such as agriculture, water supply, and flooding emergencies. This is because rainfall, which is a type of precipitation, is not easily predictable owing to several factors that include atmospheric, oceanic, and geographical factors. Therefore, a new Optune CatBoost prediction model has been developed here that can be used to predict rainfall. Some of the research techniques used include;data preprocessing, outlier analysis, normalization, SMOTE, feature engineering, and correlation analysis. This Optune CatBoost prediction gives a maximum accuracy of 96% a precision of 97% F1 score of 96.7%, and recall of 95.5%. The discrimination between rainfall and non-RA events is evaluated to be strong with the area under the ROC curve equalling 0.96. The study implies that the developed model could be used to enhance rainfall prediction, which in turn aids in decision-making on appropriate resource utilization in weather-sensitive areas. © 2024 IEEE.

关键词： Prediction models

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Enhanced Heart Disease Prediction Using Grid Search Optimized XGBoost Classifier 3

Enhanced Heart Disease Prediction Using Grid Search Optimize...

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3rd Odisha International Conference on Electrical Power engineering, Communication and Computing Technology, ODICON 2024

作者： Babu, Tina Nair, Rekha R Pavithra, K. Alliance School of Advanced Computing Alliance University Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350354379

Since heart disease is still one of the major causes of death worldwide, there has been a lot of study done on early diagnosis and prevention techniques. Thus, an XGBoost classifier tuned for Grid Search is used to carry out a novel prediction. Finding the best characteristics in the dataset is aided by Grid Search optimization. The preprocessed dataset is subjected to suitable feature selection procedures in order to extract the most pertinent predictors. The accuracy, precision, recall, and F1-score are among the performance indicators used to assess the suggested method. Comparing the suggested model to other techniques as the Random Forest model, Decision Tree model, and Multi-Layer Perceptron (MLP), it yielded an accuracy of 97.56% and a Fi_Score of 97.69%. By enabling early identification and preventive actions, this research adds to the ongoing efforts to develop accurate and dependable predictive models for cardiac disease, which in turn improves patient outcomes and lessens the strain on healthcare systems. © 2024 IEEE.

关键词： Cardiology

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A deep Q-learning model for sequential task offloading in edge AI systems

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Intelligent and Converged Networks 2024年第3期5卷 207-221页

作者： Dong Liu Shiheng Gu Xinyu Fan Xu Zheng Department of Computer Science and Engineering University of Electronic Science and Technology of ChinaChengdu 610000China Shenzhen Institute for Advanced Study University of Electronic Science and Technology of ChinaShenzhen 515100China Department of Information and Software Engineering University of Electronic Science and Technology of ChinaChengdu 610000China

Currently,edge Artificial Intelligence(AI)systems have significantly facilitated the functionalities of intelligent devices such as smartphones and smart cars,and supported diverse applications and *** fundamental supports come from continuous data analysis and computation over these *** the resource constraints of terminal devices,multi-layer edge artificial intelligence systems improve the overall computing power of the system by scheduling computing tasks to edge and cloud servers for *** efforts tend to ignore the nature of strong pipelined characteristics of processing tasks in edge AI systems,such as the encryption,decryption and consensus algorithm supporting the implementation of Blockchain ***,this paper proposes a new pipelined task scheduling algorithm(referred to as PTS-RDQN),which utilizes the system representation ability of deep reinforcement learning and integrates multiple dimensional information to achieve global task ***,a co-optimization strategy based on Rainbow Deep Q-Learning(RainbowDQN)is proposed to allocate computation tasks for mobile devices,edge and cloud servers,which is able to comprehensively consider the balance of task turnaround time,link quality,and other factors,thus effectively improving system performance and user *** addition,a task scheduling strategy based on PTS-RDQN is proposed,which is capable of realizing dynamic task allocation according to device *** results based on many simulation experiments show that the proposed method can effectively improve the resource utilization,and provide an effective task scheduling strategy for the edge computing system with cloud-edge-end architecture.

关键词： edge computing task scheduling reinforcement learning Rainbow Deep Q-Learning(RainbowDQN)

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A Survey and Experimental Review on Data Distribution Strategies for Parallel Spatial Clustering Algorithms

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Journal of computer science & Technology 2024年第3期39卷 610-636页

作者： Jagat Sesh Challa Navneet Goyal Amogh Sharma Nikhil Sreekumar Sundar Balasubramaniam Poonam Goyal Advanced Data Analytics and Parallel Technologies Laboratory Birla Institute of Technology and Science Pilani 333031India Uber New York 11101U.S.A. Computer Science and Engineering Department University of MinnesotaMinneapolis 55455U.S.A.

The advent of Big Data has led to the rapid growth in the usage of parallel clustering algorithms that work over distributed computing frameworks such as MPI,MapReduce,and *** important step for any parallel clustering algorithm is the distribution of data amongst the cluster *** step governs the methodology and performance of the entire *** typically use random,or a spatial/geometric distribution strategy like kd-tree based partitioning and grid-based partitioning,as per the requirements of the ***,these strategies are generic and are not tailor-made for any specific parallel clustering *** this paper,we give a very comprehensive literature survey of MPI-based parallel clustering algorithms with special reference to the specific data distribution strategies they *** also propose three new data distribution strategies namely Parameterized Dimensional Split for parallel density-based clustering algorithms like DBSCAN and OPTICS,Cell-Based Dimensional Split for dGridSLINK,which is a grid-based hierarchical clustering algorithm that exhibits efficiency for disjoint spatial distribution,and Projection-Based Split,which is a generic distribution *** of these preserve spatial locality,achieve disjoint partitioning,and ensure good data load *** experimental analysis shows the benefits of using the proposed data distribution strategies for algorithms they are designed for,based on which we give appropriate recommendations for their usage.

关键词： parallel data mining data distribution parallel clustering spatial locality preservation

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Low-temperature metal–oxide thin-film transistor technologies for implementing flexible electronic circuits and systems

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Journal of Semiconductors 2023年第9期44卷 3-10页

作者： Runxiao Shi Tengteng Lei Zhihe Xia Man Wong State Key Laboratory of Advanced Displays and Optoelectronics and Technologies Department of Electronic and Computer EngineeringThe Hong Kong University of Science and TechnologyHong KongChina

Here we review two 300℃metal–oxide(MO)thin-film transistor(TFT)technologies for the implementation of flexible electronic circuits and ***-enhanced TFTs for suppressing the variation and shift of turn-on voltage(VON),and dual-gate TFTs for acquiring sensor signals and modulating VON have been deployed to improve the robustness and performance of the systems in which they are *** circuit building blocks based on fluorinated TFTs have been designed,fabricated,and characterized,which demonstrate the utility of the proposed low-temperature TFT technologies for implementing flexible electronic *** construction and characterization of an analog front-end system for the acquisition of bio-potential signals and an active-matrix sensor array for the acquisition of tactile images have been reported recently.

关键词： flexible electronics metal-oxide semiconductor thin-film transistor dual gate fluorination analog front-end system sensors

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RTPD-YOLO: Reconciling YoLo-V8 Model for Real-Time Potholes Detection

RTPD-YOLO: Reconciling YoLo-V8 Model for Real-Time Potholes ...

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2024 IEEE International Conference on Machine Learning and Applied Network Technologies, ICMLANT 2024

作者： Balakrishna, Sivadi Solanki, Vijender Kumar Department of Advanced Computer Science & Engineering Vignan’s Foundation for Science Technology & Research Guntur A.P Vadlamudi India Department of Computer Science & Engineering Stanley College of Engineering and Technology for Women Telangana Hyderabad India

ISBN: (纸本)9798350389180

Indian road privilege systems employ low-quality building materials, making pothole detection and avoidance difficult. Preventing accidents requires quickly identifying and fixing potholes. Roadside potholes may damage cars and endanger drivers. This article introduces a new method for realtime pothole identification called RTPD-YOLOv8. It can identify several forms of potholes, including cracks, oil stains, patches, and stones. To begin processing, pothole videos are first transformed into picture frames. The Ultratics are applied to these frames before processing to lessen distortions. The next step is to use the pre-processed photos to detect the pothole and identify its region using the Sobel edge detector and YOLOv8. Thereafter. The proposed RTPD-YOLO will detect the potholes efficiently. The Simulation results show that Accuracy, Precision, Recall, and F1-score were used to measure the performance of the RTPD-YOLO approaches. A total Accuracy of 99.72% is attained by the RTPD-YOLO. Also, the RTPD-YOLO model gets a 98.86% precision, 94.64 percent recall, and 91.35% F1-score. When compared to other models, like Faster RCNN, SSD, and mask R CNN, the RTPD-YOLO network achieves a higher Accuracy range. © 2024 IEEE.

关键词： Edge detection

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Techno-economic analysis of green hydrogen as an energy-storage medium for commercial buildings

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Clean Energy 2023年第1期7卷 84-98页

作者： Rahul Rajeevkumar Urs Assia Chadly Ameena Al Sumaiti Ahmad Mayyas Department of Industrial and Systems Engineering Khalifa UniversityAbu Dhabi 127788UAE Department of Electrical Engineering and Computer Science Advanced Power and Energy CenterKhalifa UniversityAbu Dhabi 127788UAE

Green-hydrogen production is vital in mitigating carbon emissions and is being adopted *** its transition to a more diverse energy mix with a bigger share for renewable energy,United Arab Emirates(UAE)has committed to investing billions of dollars in the production of green *** study presents the results of the techno-economic assessment of a green-hydrogen-based commercial-building microgrid design in the *** microgrid has been designed based on the building load demand,green-hydrogen production potential utilizing solar photovoltaic(PV)energy and discrete stack reversible fuel cell electricity generation during non-PV *** the current market conditions and the hot humid climate of the UAE,a performance analysis is derived to evaluate the technical and economic feasibility of this *** study aims at maximizing both the building microgrid’s independence from the main grid and its renewable *** results indicate that the designed system is capable of meeting three-quarters of its load demand independently from the main grid and is supported by a 78%renewable-energy *** economic analysis demonstrates a 3.117-$/kg levelized cost of hydrogen production and a 0.248-$/kWh levelized cost for storing hydrogen as ***,the levelized cost of system energy was found to be less than the current utility costs in the *** analysis shows the significant impact of the capital cost and discount rate on the levelized cost of hydrogen generation and storage.

关键词： fuel cell electrolyser green hydrogen microgrid photovoltaic

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advanced U-Net++ Architecture for Precise Brain Tumor Segmentation in MRI Images: A Robust Solution for Medical Image Analysis

Advanced U-Net++ Architecture for Precise Brain Tumor Segmen...

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

作者： Nair, Rekha R Babu, Tina Ramasamy, Gayatri Singh, Tripty Yuan, Xiaohui Alliance University Alliance School of Advanced Computing Department of Computer Science and Engineering Bengaluru India Amrita School of Computing Bengaluru Amrita Vishwavidyapeetham Department of Computer Science and Engineering India University of North Texas Department of Computer Science and Engineering DentonTX United States

ISBN: (纸本)9798350385199

Tumor segmentation in MRI images is a crucial process in medical imaging, aimed at accurately identifying and delineating tumor regions within the brain or other tissues. Hence proposed a modified U-Net++ based segmentation with predicted mask. To further improve the performance of the proposed method, incorporated additional layers into the U Net++ architecture. U-Net++ consists of Encoder Decoder and the encoder captures image content, while the decoder delineates object boundaries. U-Net++ excels in capturing both high-level and intricate details, which significantly boosts segmentation performance. This helps in accurate tumor segmentation of complex MRI images. To measure the effectiveness of the proposed model for segmentation and detection, Accuracy, Intersection over Union(IOU), Dice Coefficient and Area under the ROC Curve(AUC) scores are used. The proposed U-Net++ brought accuracy to 99 percent, which could significantly help in the process of accurate segmentation of human tissues. Accuracy of 39 % for IOU of 80, Dice Coefficient of 89%, and 33% for the test set. This is equivalent to 8% sensitivity and accuracy and AUC of 78. The proposed method can be used as an additional diagnosis aid to help doctors determine the suitability of the treatment and the changes occurring in the body during the development of the disease © 2024 IEEE.

关键词： Magnetic resonance imaging

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GAN-Alz: Synthetic Data Generation for Multiclass Alzheimer's Classification

GAN-Alz: Synthetic Data Generation for Multiclass Alzheimer'...

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

作者： Nair, Rekha R Babu, Tina Ramasamy, Gayatri Singh, Tripty Yuan, Xiaohui Alliance University Alliance School of Advanced Computing Department of Computer Science and Engineering Bengaluru India Amrita Vishwavidyapeetham Amrita School of Computing Department of Computer Science and Engineering Bengaluru India University of North Texas Denton Department of Computer Science and Engineering TX United States

ISBN: (纸本)9798350385199

Accurate detection of skin cancer, particularly melanoma, is crucial for effective treatment and patient survival. This study explores the use of Convolutional Neural Networks (CNNs) enhanced by Generative Adversarial Networks (GANs) for skin cancer detection. CNNs have shown remarkable performance in image classification tasks but often require extensive labeled datasets, which are scarce in medical domains. To address this, GANs are employed to generate synthetic skin lesion images, augmenting the dataset and improving the model's generalization capability. The proposed approach integrates a pre-trained CNN architecture fine-tuned with a combination of real and GANgenerated images, enhancing the network's ability to distinguish between benign and malignant lesions. Experimental results demonstrate that the inclusion of GAN-augmented data improves the model's accuracy by 7%, achieving an overall accuracy of 94%, with precision and recall rates of 92% and 93%, respectively. These findings suggest that the GAN-augmented CNN model offers a promising solution for automated skin cancer screening © 2024 IEEE.

关键词： Generative adversarial networks

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