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Leukemia Cancer Classification from Abnormal Cell Images Using a Deep Learning Approach: A Comprehensive Study 26

Leukemia Cancer Classification from Abnormal Cell Images Usi...

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26th International Conference on computer and Information Technology, ICCIT 2023

作者： Afrin, Sadia Paul, Subrata Kumer Paul, Rakhi Rani Haque, Md. Momenul Kabir, Md Shahriar Fahim, Sultan Islam, Md. Kamrul Dept. of Computer Science and Engineering Natore6431 Bangladesh

ISBN: (纸本)9798350359015

Leukemia, a malignant blood cancer, is characterized by the uncontrolled proliferation of abnormal white blood cells in the bone marrow and blood. It is a life-threatening disease with various subtypes, making precise classification crucial for effective treatment. However, accurate classification of leukemia subtypes from abnormal cell images poses a challenging problem due to the complex and subtle variations in cell morphology. In response to this challenge, this comprehensive study employs an innovative machine learning approach, utilizing the EfficientNet B3 architecture. With this model, we achieved an impressive accuracy rate of 98.95% and a minimal loss of 0.120, demonstrating its efficacy in classifying leukemia subtypes. To conduct our experiments, we utilized the Kaggle Leukemia cancer dataset, ensuring a robust and reliable evaluation of our proposed methodology. This research contributes significantly to the development of efficient tools for early diagnosis and personalized treatment strategies for leukemia. © 2023 IEEE.

关键词： Acute Lymphoblastic Leukemia (ALL) Deep Learning EfficientNet B3 Leukemia cancer

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Enhanced Adaptive Video Streaming for Resource-Constrained UAVs through Dynamic Region-of-Interest Tile Swapping 10

Enhanced Adaptive Video Streaming for Resource-Constrained U...

引用

10th International Conference on Applied System Innovation, ICASI 2024

作者： Hsu, Tz-Heng Ju, Rui-Han Horng, Gwo-Jiun Southern Taiwan University of Science and Technology Dept. of Computer Science and Information Engineering Tainan Taiwan

ISBN: (纸本)9798350394924

Real-time video transmission via unmanned aerial vehicles (UAVs) is significantly impacted by latency issues. Using Region of Interest (ROI) tile segmentation methods, video streaming techniques can dynamically adjust the encoding bitrate, assign various priorities to distinct video tiles, and specify different quality quantization factors. High-efficiency video tile encoding techniques, combined with ROI-based streaming methods, can meet various Quality of Service (QoS) criteria. However, dynamically assigning quantization factors for different tiles has the disadvantage of increasing the encoding system's complexity. This study presents a method to dynamically swap ROI tiles to lower the complexity of the encoding system, making it suitable for UAVs with limited computational capacities. By adopting the proposed adaptive video streaming transmission method with tile-swapping strategy, it is feasible to enhance the visual quality in critical areas for resource-constrained UAV devices. © 2024 IEEE.

关键词： Video streaming

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Review on Brain Tumor Detection using Custom CNN Layers and Transfer Learning on MRI images 14

Review on Brain Tumor Detection using Custom CNN Layers and ...

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14th International Conference on Computing Communication and Networking Technologies, ICCCNT 2023

作者： Saxena, Akshit Singh, Jaspreet Chandigarh University Dept. of Computer Science and Engineering India

ISBN: (纸本)9798350335095

Early diagnosis and treatment planning are greatly aided by the early identification of brain tumors. Because of its high resolution, low radiation, and low risk of patient discomfort, magnetic resonance imaging (MRI) is frequently used to diagnose brain tumors. Brain tumor identification is only one area where recent developments in convolutional neural networks (CNNs) have shown exceptional effectiveness. This article summarizes recent progress in detecting brain tumors by utilizing MRI images and bespoke CNN layers with transfer learning. The review kicks off with a discussion of the difficulties of detecting brain cancers, such as the tumors' complexity and heterogeneity and the scarcity of available annotated data. The article proceeds to go into the foundations of CNNs and their applicability to MRI image processing. To improve detection accuracy, we incorporate custom CNN layers that are tailored to capture salient tumor-specific information. The concept of transfer learning, in which CNN models trained on large-scale datasets are repurposed for brain tumor detection, is also discussed at length in the review. Using transfer learning, we can take advantage of what we've learned about general image identification to better train models to spot brain tumors. Fine-tuning, feature extraction, and other transfer learning methods are addressed at length. Recent research using custom CNN layers and transfer learning approaches to detect brain cancers in MRI images is thoroughly analyzed in this study. Among the benefits and drawbacks discussed are the methods' adaptability to small datasets, enhanced detection accuracy, and decreased training time. Also, the significance of using metrics for measuring performance and benchmark datasets for comparing methods fairly is discussed. The analysis concludes with suggestions for future study, such as the combination of functional and diffusion tensor imaging with conventional MRI scans to better detect brain tumors. Further

关键词： Magnetic resonance imaging

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Unmasking Frame Duplication: Comprehensive Approach to Detection in Digital Video Using Machine Learning Algorithms and Signal Processing Techniques

Unmasking Frame Duplication: Comprehensive Approach to Detec...

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

作者： Praddipraj, N.N. Praveen Kumar, B. Sreeji, S. Sathyabama Institute Of Science And Technology Dept. Of Computer Science And Engineering Tamilnadu Chennai600119 India

ISBN: (纸本)9798350364828

Our research proposes a comprehensive approach to identify duplicate frames in digital videos. It integrates machine learning and signal processing techniques for effective identification. The process begins with pre-processing, enhancing video quality through temporal filtering and noise reduction. Next, feature extraction captures statistical and structural information from each frame. Deep neural networks and other machine learning methods are used to classify frames as original or duplicated using these features. To enhance detection accuracy, signal processing methods like block-based and key point-based approaches are applied. Block-based methods divide frames into segments for region comparison, while key point-based methods identify and match key points between frames. Our method's effectiveness and robustness are demonstrated through experiments on various digital videos. This combination of machine learning and signal processing offers a comprehensive solution to uncover frame duplication, ensuring video content's integrity and authenticity. © 2024 IEEE.

关键词： Learning algorithms

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Classification and Prediction of Stroke Disease Using Machine Learning Algorithms 7

Classification and Prediction of Stroke Disease Using Machin...

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7th International Conference on Circuit Power and Computing Technologies, ICCPCT 2024

作者： Lahari, Podutur Kumar, Akarsh Nandini, D. Usha Praveena, M. D. Anto Selvan, Mercy Paul Sathyabama Institute of Science and Technology Dept. of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350372816

Using text mining tools and machine learning algorithms, the paper presents a prototype for classifying strokes. The significance of machine learning extends across various domains, including surveillance, medicine, and data management, where appropriately trained algorithms prove invaluable. The study incorporates data mining techniques, offering a comprehensive understanding of information tracking from both semantic and syntactic perspectives. By analyzing medical case sheets, the proposed approach extracts symptoms and trains the system based on that information. A total of 507 case sheets are collected at "Sugam Multispecialty Hospital"in "Kumbakonam", Tamil Nadu, India, during the data collection phase. A tagging and maximum entropy approach was used to mine these case sheets. Stroke classification relies heavily on the suggested stemmer for distinguishing between common and special features. To handle the data that has been processed, support vector machines, XG Boost algorithms, SGD algorithms, decision trees, and random forests were used as machine learning algorithms. Random Forest demonstrated the highest accuracy among stroke classification algorithms evaluated. This integration of text mining and machine learning offers a promising framework for enhancing stroke classification based on patients' symptoms extracted from medical case sheets. The synergy between these technologies provides a robust methodology that can contribute significantly to improving the efficiency and accuracy of stroke diagnosis and classification in a medical. © 2024 IEEE.

关键词： Semantics

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Covid-19 Knowledge Graph Generation and Enabling Analysis across Healthcare, Socioeconomic, and Environmental Dimensions 26

Covid-19 Knowledge Graph Generation and Enabling Analysis ac...

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26th International Conference on computer and Information Technology, ICCIT 2023

作者： Shafkat Raihan, S.M. Pratap Deb Nath, Rudra Das, Tonmoy Chandro University of Chittagong Dept. of Computer Science and Engineering Bangladesh

ISBN: (纸本)9798350359015

There are various significant factors that influence Covid-19 pandemic, such as health, socioeconomic, and environmental aspects. Researchers and administrators require better data on measures such as confirmed cases and deaths due to Covid-19 to efficiently withhold its spread. While existing Covid-19 data resources often focus on spatio-temporal aspects, most research neglects the combined impact of multiple factors. This study addresses this gap by modeling and analyzing Covid-19 epidemiological data across dimensions like location, comorbidity, and occupation. We use data warehousing (DW) technology to model and integrate data from diverse sources in the multidimensional (MD) format. Then, we enable Online Analytical Processing (OLAP)-like analysis on DWs to derive Covid-19 related insights. Besides, to make the data interoperable, and accessible, data need to be annotated, integrated, and published semantically in accordance to FAIR principles. To accomplish this, the data is modeled as a knowledge graph (KG), where the semantic relationships among data are captured in the integration process. The KG is implemented using the Resource Description Framework (RDF) model, and semantically linked with external datasets. The generated knowledge graph is analyzed through an interactive analytical interface to observe the Covid-19 confirmed cases and deaths from thirteen aspects. The performance and quality of the knowledge graph is assessed against prominent data stores modeling Covid-19 data. © 2023 IEEE.

关键词： Big Data covid-19 Data warehousing FAIR Linked Data OLAP

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Reconsider Time Series Analysis for Insider Threat Detection

Reconsider Time Series Analysis for Insider Threat Detection

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2024 IEEE International Conference on Big Data, BigData 2024

作者： Chen, Chia-Cheng Pao, Hsing-Kuo National Taiwan University of Science and Technology Dept. of Computer Science and Information Engineering Taipei Taiwan

ISBN: (纸本)9798350362480

Insider threat detection (ITD) presents a significant challenge in cybersecurity, particularly within large and complex organizations. Traditionally, ITD has been overshadowed by the focus of external threats, resulting in less attention and development in this critical area. Conventional ITD approaches often rely heavily on event-driven approaches. On top of that, researchers developed various rule-based methods to conquer the tasks. Based on that, we often ignore the intrinsic temporal relationships that are naturally built in between events that occur in different moments. For instance, we may easily understand events with causality such as one anomalous event followed by another specific event to complete a malicious action;however, may not be aware of events that occur around 9 am every morning during working hours. In our opinion, we attempt to re-consider the temporal behavior to extract the information hidden in cyberspace activities. Specifically, some effective sentence embeddings can assist us in providing informative internal representations to summarize temporal behaviors in the temporal activity sequences to make the right judgment on insider threat detection. In this paper, we propose a novel methodology for insider threat detection that emphasizes temporal relationship modeling on top of already-matured event sequence analysis to effectively catch insider threats. The proposed approach leverages contrastive sentence embeddings to learn users' intentions in sequences, followed by the deployment of a user-level and event-level Contrastive Learning (euCL) model to incorporate temporal behaviors with user behavior embeddings. To validate the proposed methodology, we conduct extensive analyses and experiments using the publicly available CERT dataset. The results demonstrate the effectiveness and robustness of the proposed method in detecting insider threats and identifying malicious scenarios, highlighting its potential for enhancing cybersecurity measur

关键词： Contrastive Learning

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A Heart Disease Prediction Model using Merged XGBoost-SVM Classifier and Particle Swarm Optimization 5

A Heart Disease Prediction Model using Merged XGBoost-SVM Cl...

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5th International Conference on Mobile Computing and Sustainable Informatics, ICMCSI 2024

作者： Majumder, Annwesha Banerjee Gupta, Somsubhra Majumder, Sourav Singh, Dharmpal JIS College of Engineering Dept. of Information Technology Kalyani India Swami Vivekananda University Dept. of Comouter Science Kolkata India Capgemini Kolkata India JIS College of Engineering Dept. of Computer Science and Engineering Kalyani India

ISBN: (纸本)9798350395235

This study aims to develop an advanced heart disease prediction model using machine learning techniques to address the global health challenge of early identification and accurate diagnosis, especially considering the projected deficits in healthcare by 2030. The proposed model integrates two powerful algorithms, XGBoost and Support Vector Machine Classifier, leveraging their complementary strengths to achieve effective classification. To enhance the model's performance, feature selection has been conducted using the LASSO method, which identifies the most relevant features, while outlier removal based on the Z score ensures data quality. StandardScaler has been utilized to standardize the data, mitigating any biases caused by differing scales. Furthermore, Particle Swarm Optimization has been employed to optimize the model's performance by fine-tuning its parameters. The reported accuracy of 91.8% underscores the model's potential to accurately predict heart disease, offering valuable support in prevention efforts. These findings highlight the significance of technological advancements in healthcare, particularly in addressing the growing demand for accurate and timely diagnoses. © 2024 IEEE.

关键词： Support vector machines

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Student Performance Analysis with Ensemble Progressive Prediction 5

Student Performance Analysis with Ensemble Progressive Predi...

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5th International Conference on Smart Systems and Inventive Technology, ICSSIT 2023

作者： Althaph, B. Sreenivasu, S.V.N. Reddy, D. Venkata Dept. of Computer Science and Engineering Andhra Pradesh India

ISBN: (纸本)9781665474672

It is essential to have an accurate prediction of students' future performance in order to properly carry out the necessary pedagogical interventions that are required to assure students will graduate on time and with an acceptable degree. Even though there is a wealth of research on the topic of predicting student performance when it comes to finding solutions to problems or preparing for classes by utilizing datadriven methods, the topic of predicting student performance when it comes to completing degrees (for example, college programs) is much less researched and faces new challenges, there is a large amount of diversity among students in terms of their prior experiences and the courses they choose to take. The students' developing progress should be factored into the prediction. This study proposes a unique machine learning technique for forecasting student success in degree programs. This method may handle these important issues, and it is one of the main contributions of this research. The suggested technique is distinguished primarily by its two components. To begin, a structure with two layers, a bi-layered structure, is constructed for the purpose of creating predictions based on the changing performance states of students. Then, a strategy that is driven by data and is based on latent component models and Ensemble Progressive Prediction (EPP) based matrix factorization is suggested for the purpose of determining the relevance of the course, which is essential for the construction of effective base predictors. The proposed strategy achieves better performance than benchmark methods by conducting extensive simulations using a dataset of UCLA undergraduate student data that was gathered over the course of three years. © 2023 IEEE.

关键词： Forecasting

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Adchat-TVQA: Innovative Application of LLMs-Based Text-Visual Question Answering Method in Advertising Legal Compliance Review 5

Adchat-TVQA: Innovative Application of LLMs-Based Text-Visua...

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5th International Conference on Machine Learning and computer Application, ICMLCA 2024

作者： Gao, Ruiling Hou, Yingqi Tang, Weijie Zhang, Jin Hu, Yanbo Huang, Haiyun Tan, Wen'an Li, Liping Shanghai Polytechnic University Dept. of Computer Engineering Shanghai China Donghua University Dept. of Computer Science Shanghai China Tufts University Dept. of Computer Science MedfordMA United States

ISBN: (纸本)9798331530334

Advertising legal compliance reviews have always been time-consuming and labor-intensive, and existing Large Language Models(LLMs) are far worse performing than senior industry experts. In this paper, we propose a novel LLMs-based text-visual question answering method called Adchat-TVQA for advertising legal compliance review. After reading an advertising image and the corresponding review question, it will first understand the content on the image through multimodal learning, then optimize the question with Zero-Shot Chain-of-Thought (CoT) prompting method based on predefined industry expert experience, and finally input the augmented question into the large language model for inquiry. The feasibility of this method is verified through system prototype implementation and end-to-end functional tests. In addition, we summarized the characteristics of advertising images and added image segmentation and text box transpose to the data processing process for system performance improvement. Performance testing of the text visual cognitive question task on the AIWIN2021 dataset shows that the method scores higher than Microsoft's LayoutLM, with an increasement of the score from 51.52% to 52.74%. © 2024 IEEE.

关键词： Marketing

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