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检索条件"任意字段=International Conference on Computer Engineering and Technology"

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A hybrid CNN-LSTM approach for detecting intracerebral hemorrhage in CT images

A hybrid CNN-LSTM approach for detecting intracerebral hemor...

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2025 international conference on computer, Electrical and Communication engineering, ICCECE 2025

作者： Dash, Pratyusa Sabut, Sukanta Kumar Mishra, Ruby Mishra, B.S.P. Heritage Institute of Technology Department of Cse Westbengal Kolkata India Kalinga Institute of Industrial Technology School of Electronics Engineering Odisha Bhubaneswar India Kalinga Institute of Industrial Technology School of Mechanical Engineering Odisha Bhubaneswar India Kalinga Institute of Industrial Technology School of Computer Engineering Odisha Bhubaneswar India

ISBN: (纸本)9798331513894

A Stroke is a life-risk condition caused due to lack in blood flow to the part of the brain. Accurate diagnosis using computed tomographic (CT) scans is crucial for immediate care in the emergency department. A multidisciplinary approach is essential to manage this health challenge. Medical imaging using computed tomography (CT) and MRI data from stroke patients plays a crucial role in supporting clinical decision-making. Locating lesions in image is a challenging and time-consuming task for radiologists, hence automated techniques powered by Artificial Intelligence (AI) is vital for detecting and quantifying Intracerebral Hemorrhage (ICH). In this study, we proposed deep learning approach based on CNN along with long short-term memory (CNN-LSTM) for accurate prediction of ICH on CT scans. Non-contrast whole-head CT scan images were collected from 72 hemorrhagic stroke patients at KIMS, Bhubaneswar. A total of 2,394 head CT images were used to train the models with both CNN and CNN-LSTM architectures with a split of 80:20. The performance of the two models was then compared to evaluate their effectiveness. The CNN alone achieved an accuracy of 0.95, a recall of 0.89, and an F1-score of 0.94, whereas the hybrid CNN-LSTM model outperformed it with an accuracy of 0.97, a recall of 0.97, and an F1-score of 0.97. The findings indicate that the hybrid CNN-LSTM model performs better in detecting ICH lesions in CT images. Further research is essential to enhance the detection of hemorrhagic stroke in clinical settings for effective management of stroke. © 2025 IEEE.

关键词： computerized tomography

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AI-Powered Advertisement Design: A PIL-Based Approach for Quality and Performance Analysis 3

AI-Powered Advertisement Design: A PIL-Based Approach for Qu...

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

作者： Chilupuri, Harshitha Ramesh, G. Praveen, J. Gude, Venkataramaiah Gokaraju Rangaraju Institute of Engineering and Technology Dept. of Artificial Intelligence and Machine Learning Hyderabad India Gokaraju Rangaraju Institute of Engineering and Technology Dept. of Computer Science and Engineering Hyderabad500090 India Gokaraju Rangaraju Institute of Engineering and Technology Department of Electrical and Electronics Engineering Telangana Hyderabad500 090 India Gp Technologies Llc United States

ISBN: (纸本)9798331515911

The paper presents the development of a Data Science solution that applies generative AI models in order to create high-quality images to be used in visual marketing and advertising. Using the Python library PIL (Pillow) along with machine learning frameworks, the solution combines predefined product images with the selected backgrounds, enhancing image synthesis techniques for more appealing and contextually relevant visuals. This leads to improved efficiency and effectiveness in the creation of content by automatically placing product images onto the background images used in marketing campaigns. The approach demonstrated a 98.06% SSIM (Structural similarity Index) score, indicating significant visual quality improvement. Initial results suggest a 15% enhancement in visual appeal compared to traditional image creation methods. © 2025 IEEE.

关键词： Data Science

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Regression analysis based on driving schools in Haikou 24

Regression analysis based on driving schools in Haikou

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8th international conference on Electronic Information technology and computer engineering, EITCE 2024

作者： Yang, Darun Yang, Daqing Yang, Dashuai Yang, Dacai College of Information Engineering Hainan Vocational University of Science and Technology Hainan Haikou China Longjie Town Tianqiao Primary School Guizhou Bijie China College of Humanities and Technology Guizhou University for Nationalities Guizhou Guiyang China Guiyang University of Information Technology Guizhou Guiyang China

ISBN: (纸本)9798400710094

With the development of society and the growth of population in Haikou city, the driving school market in Haikou faces a series of challenges such as unreasonable teaching methods, cumbersome registration processes, non-standard training sites, overly long learning periods, delayed handling of student complaints, and lack of supervision. Using univariate regression and ridge regression to analyze the relationship between the number of exam takers and the number of passers, predicting the number of passers with a certain number of exam takers, providing reference for Haikou local undergraduate students and social individuals when registering for driving schools. Also, it aims to provide scientific decision-making basis for driving school managers, decision-makers, and practitioners, and predict future trends. © 2024 Copyright held by the owner/author(s).

关键词： Students

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A Deep Learning Based System to Predict the Plant Disease Using Streamlit 4

A Deep Learning Based System to Predict the Plant Disease Us...

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4th international conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Warbhe, Mohan K. Bore, Joy Jordan Chaudari, Shiv Nath Faculty of Engineering and Technology Department of Computer Science and Design Maharashtra Sawangi Wardha442001 India Faculty of Engineering and Technology Department of Computer Science and Medical Engineering MaharashtraSawangi Wardha442001 India

ISBN: (纸本)9798331523923

The proposed web application for tomato leaf disease detection exemplifies the transformative power of Artificial Intelligence and computer Vision in modern agriculture. Addressing the critical issue of early and accurate disease detection, this solution bridges the gap between advanced technological innovations and the practical needs of farming. The application is designed using Streamlit and Python to combine the usability of a friendly interface with the power of robust analytical capabilities in CNN, offering real-time reliable diagnosis and actionable insights to farmers. This application provides the highest possible accuracy and adaptability through the thorough training and evaluation of models and is a good asset for disease management. Deployment on scalable cloud platforms ensures access, reliability, and global reach. Future improvements, including extending disease detection to other crops, multilingual support, real-time recommendations, and integration with IoT devices, promise to expand its reach and utility. This initiative is a shining example of how AI-based solutions can transform agricultural practices by enhancing productivity, reducing losses, and ensuring sustainability. The application empowers farmers and contributes to a resilient agricultural ecosystem, addressing the pressing need for innovative and sustainable solutions in a rapidly evolving world. © 2025 IEEE.

关键词： Smart agriculture

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Flexible Hybrid Electronics for Printing of Nano and Chip Scale Structures 1

Flexible Hybrid Electronics for Printing of Nano and Chip Sc...

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1st international conference on Advances in computer Science, Electrical, Electronics, and Communication Technologies, CE2CT 2025

作者： Singh, Neelima Gupta, Radha Singh, Rakesh Kumar Shruthi, T.S. Gupta, Parbhat Singhal, Abhinav Uttaranchal University Department of Computer Science and Engineering Dehradun India Dayananda Sagar College of Engineering Department of Mathematics Bangalore560078 India IIMT College of Engineering U.P. Greater Noida India Nitte Meenakshi Institute of Technology Department of Mathematics Karnataka Bangalore India SRM Institute of Science and Technology Delhi NCR Campus Ghaziabad Modinagar India Tula's Institute Department of Computer Science and Engineering Dehradun248197 India

ISBN: (纸本)9798331518578

Adaptable sensors, circuits, and substrates are joined with unbending electronic parts to make adaptable flexible hybrid electronics (FHE). The printing of nanomaterials has benefits over the costly, multi-step, and blunder inclined cleanroom-based nano-microfabrication process, like expense adequacy, high-throughput, steadfastness, and adaptability. FHE usually consists of devices that range in size from nano to chip, and it might be difficult to integrate them using a standard procedure. The mix of ultrathin chips and nanowires (NWs) based electronic layers on a solitary substrate is shown here utilizing printed gadgets. The performance of the transistors on UTCs (ultrathin chips) is thoroughly studied in order to optimise the printing process. The adequacy of the printing system is affirmed by electrical information accumulated from 14 semiconductors on 3 unmistakable chips, which uncover next to no adjustment of execution following exchange printing. Building UV photodetectors utilizing DIW printing further exhibits the unrivaled grade nature of ZnO-NWs-put together electronic layers delivered with respect to a similar substrate. © 2025 IEEE.

关键词： Substrates

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Features enhancement of micro-service based cab booking system using block-chain 4th

Features enhancement of micro-service based cab booking syst...

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4th international conference on Computational Methods in Science and technology, ICCMST 2024

作者： Rohit Singh, Neelaksh Shukla, Riya Singh, Ashutosh Kumar Department of Computer Science and Engineering Institute of Engineering and Rural Technology Prayagraj India Department of Computer Science and Engineering United College of Engineering and Research Prayagraj India

ISBN: (纸本)9781032911571

Cab booking services help people order taxis. Existing cab booking services use client server-based architecture. The paper gives a study of the architecture and workings of the Uber cab booking website (Dissanayake, March 18, 2021). It has a micro-service based architecture (Li et al. 2021). Load balancing (Al-Jaroodi et al. 2012) and stream processing (Besnard et al. 2013) are used to handle a large number of requests. The workload is divided among servers using Dispatch Optimization (Abid et al. 2022). It ensures fast responses, easy connection minimal downtime, and efficient use of resources. However, the use of servers increases the cost of service. Rider and driver depend on the server to connect. They don’t know if the system is charging them fairly. It reduces transparency and trust. The paper proposes a blockchain-based cab booking application to solve some of the problems. Trust, transparency, and decentralization can be ensured using blockchain. Transparent and immutable ledgers of blockchain ensure trust and reduce reliance on intermediaries. Smart contracts govern ride management, optimizing efficiency. Blockchain capabilities are used to ensure fair rides, direct interaction of passengers with drivers, and automated payments. The proposed system aims at combining existing technology with blockchain solution to ensure security and transparency along with scalability and quick response time. © 2025 the Author(s).

关键词： Reservation systems

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Interpretable Grade Prediction Based on ECOC and Shapley Theory 24

Interpretable Grade Prediction Based on ECOC and Shapley The...

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8th international conference on Electronic Information technology and computer engineering, EITCE 2024

作者： Yang, Dapeng Cao, Longhan Pan, Lifang Xie, Shutong School of Computer Engineering Jimei University Fujian Xiamen China School of Science Jimei University Fujian Xiamen China

ISBN: (纸本)9798400710094

In recent years, blended learning models combining online and offline instructional methods have been widely implemented in higher education institutions. Online learning platforms accumulate a large number of fine-grained records of students’ learning behavior, offering an excellent opportunity to analyze students’ learning behavior. Firstly, a year’s worth of student behavior log data was collected from the "Java Language Programming" blended learning course at a college. Secondly, we propose an interpretable grade prediction method based on the error correcting output code (ECOC) with Shapley theory. This approach employs Recursive Feature Elimination (RFE) to select features from the original features, and creates an ECOC classification matrix to predict student performance. Finally, the Shapley values for each feature were used to conduct an explainable analysis of the prediction results. Experimental results show that the proposed method outperforms other classical classification algorithms. This study provides a visual and educational analysis of the major factors affecting academic performance, thereby offering educational administrators more intuitive decision support. © 2024 Copyright held by the owner/author(s).

关键词： Adversarial machine learning

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Visualizing and analyzing the impact of Chinese forests on the environment using a knowledge graph 24

Visualizing and analyzing the impact of Chinese forests on t...

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8th international conference on Electronic Information technology and computer engineering, EITCE 2024

作者： Wen, Xinyuan Shi, Jinmei Chen, Shasha Hainan Vocational University of Science and Technology Hainan Haikou China

ISBN: (纸本)9798400710094

Forests are often referred to as the"lungs of the Earth." In the past, driven by the pursuit of profits, humans extensively deforested, leading to significant damage to China’s forest resources. This deforestation has caused a series of issues such as resource scarcity, land desertification, and poor air quality. By collecting data from the Seventh, Eighth, and Ninth National Forest Resources Inventory Reports, as well as data on climate change, natural disasters, and species richness across different regions in the past 20 years, this study aims to conduct a knowledge graph analysis and data visualization using Python and Finebi. The goal is to analyze and identify the direct and indirect factors that influence changes in China’s forests, as well as the environmental and economic impacts of these changes. Through the analysis of relevant data and literature review, this paper provides recommendations for sustainable forest development in China, aiming to raise awareness of environmental degradation. © 2024 Copyright held by the owner/author(s).

关键词： Forest ecology

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Explainable AI for Leukemia Diagnosis: Modified ResNet with Hard Attention Mechanisms 1

Explainable AI for Leukemia Diagnosis: Modified ResNet with ...

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1st international conference on Intelligent Systems and Computational Networks, ICISCN 2025

作者： Ramanujam, E. Sekar, K. Bavashree, G.S. Prabakar, Shreya Sai National Institute of Technology Department of Computer Science and Engineering Silchar India Rajalakshmi Engineering College Department of Aiml Chennai India

ISBN: (纸本)9798331529246

Leukemia is a highly complex and aggressive form of cancer, necessitating accurate and timely diagnosis for effective treatment. This study investigates the use of Explainable Artificial Intelligence (XAI) techniques to enhance computer-aided diagnosis (CAD) systems for leukemia. A modified ResNet architecture, integrated with a hard attention mechanism, is proposed to improve model performance and interpretability. The approach emphasizes robust segmentation of white blood cell (WBC) nuclei, utilizing advanced image processing techniques and U-Net models for precise feature extraction. Model evaluation focuses on both accuracy and interpretability, employing Grad-CAM and clustering space analysis to assess network focus and transparency in decision-making. The results underscore the strengths and limitations of the proposed architecture, offering insights into further advancements in XAI to improve the reliability and transparency of CAD systems. This work ultimately aims to advance clinical outcomes in leukemia diagnosis by providing more reliable and interpretable diagnostic tools. The proposed model produces 99.7% of accuracy for detection and classification of leukemia. © 2025 IEEE.

关键词： Diseases

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Plant Leaf Disease Identification based on Machine and Deep Learning Methods 3

Plant Leaf Disease Identification based on Machine and Deep ...

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3rd international conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Kalaiarasi, P. Ramalakshmi, R. Kannan, V. Bhuvanesh, A. Department of Computer Science and Engineering Kalasalingam Academy of Research and Education Krishnankoil India Department of Agriculture Kalasalingam Academy of Research and Education Krishnankoil India Department of Electrical and Electronics Engineering PSN College of Engineering and Technology Tirunelveli India

ISBN: (纸本)9798331527549

The substantial effects of plant diseases on agriculture, which result in losses in production of crops, financial losses, and food insecurity, highlight the necessity for plant leaf disease detection and categorization. In order to effectively control illnesses, stop them from spreading to other plants, and develop tailored treatment plans, early and precise disease identification is essential. Various strategies are used for plant leaf disease diagnosis, such as manual symptom assessment, computer image processing, and visual examination by skilled professionals. Traditional methods rely on human expertise to visually identify disease symptoms based on characteristic patterns, discolorations, or lesions on plant leaves. These techniques, however, are labor-intensive, time-consuming, and sensitive to bias and mistakes. The development of automated and computer-based techniques for the identification of plant leaf disease through the use of ML (machine learning) algorithms and digital imaging technology has garnered increasing attention in recent years. There are still a number of issues with the DL (deep learning) and ML algorithms. In order to identify research gaps and address them in the future while developing a unique framework, this study presents an examination of ML and DL models. © 2025 IEEE.

关键词： Deep learning

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