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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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Revolutionizing Requirements Elicitation: Deep Learning-Based Classification of Functional and Non-Functional Requirements 5

Revolutionizing Requirements Elicitation: Deep Learning-Base...

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5th IEEE International Conference on Pattern Recognition and Machine Learning, PRML 2024

作者： Hanna, Grace Boyar, Nathan Garay, Nathan Maleki, Mina University of Detroit Mercy Dept. of Electrical and Computer Engineering and Computer Science DetroitMI United States

ISBN: (纸本)9798350355925

The requirements elicitation phase in the software development life cycle (SDLC) is both critical and challenging, especially in the context of big data and rapid technological advancement. Traditional approaches like workshops and proto-typing, while useful, often struggle to keep pace with the massive data volumes and rapidly changing user demands characteristic of modern technology. This paper introduces a data-driven approach that utilizes deep learning (DL) and natural language processing (NLP) to enhance the requirements elicitation process by extracting requirements and classifying them into functional and non-functional categories. Our research involves a deep neural network (DNN) trained on a large dataset of transcriptions from client/user stories. This DNN can identify whether a specific text represents a functional requirement, a non-functional requirement, or neither. Our approach shows a marked improvement over previous methods, with a 33% increase in accuracy and an 18% increase in the F1 score. These results indicate the capability for deep learning techniques to play a vital role in elicitation. © 2024 IEEE.

关键词： Software design

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Job Applications Selection and Identification: Study of Resumes with Natural Language Processing and Machine Learning

Job Applications Selection and Identification: Study of Resu...

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2023 IEEE International Students' Conference on Electrical, Electronics and computer science, SCEECS 2023

作者： Pimpalkar, Amit Lalwani, Aastha Chaudhari, Roshan Inshall, Mohd. Dalwani, Mahak Saluja, Tarandeep Jit Dept. of Computer Science Engineering Nagpur India

ISBN: (纸本)9798350398748

For each job ad, the organization received a high number of applicants. Finding the right person's application from various applications is a time-consuming task for any company these days. The categorization of a representative's resume is a time-consuming, labor-intensive, and resource-intensive procedure. Natural language processing and advanced analytics can understand and analyze unstructured written material to extract the required information. The idea is to teach the machine to study text in the same manner that people do. By reviewing applications with natural language processing and machine-learning processing, we may be able to locate people with the abilities and attributes we need more quickly. This study provides a comprehensive assessment of resume selection and identifies the existing work gaps. Different algorithms for machine learning and methodologies for analyzing and interpreting unstructured data have been researched. This study also addresses the research problems and future potential of resume analysis regarding the writing style, word choice, and grammar of unstructured written communication. © 2023 IEEE.

关键词： Machine learning

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MetaSPICE: Metaprogramming SPICE Framework for the Design Space Exploration of PUF Circuits 12

MetaSPICE: Metaprogramming SPICE Framework for the Design Sp...

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12th International Conference on Modern Circuits and Systems Technologies, MOCAST 2023

作者： Xynos, Athanasios Tenentes, Vasileios University of Ioannina Dept. of Computer Science & Engineering Greece

ISBN: (纸本)9798350321074

Silicon Physical Unclonable Functions (PUFs) generate challenge-response pairs that are unique to each die, allowing for secure identification, authentication and cryptographic keys generation. However, designing and evaluating PUFs is a complex task that can require a vast amount of SPICE netlists simulation. In this paper, we present MetaSPICE, which is a novel metaprogramming SPICE framework for the design space exploration of PUFs. Metaprogramming refers to the process of writing programs that manipulate other programs, data structures or even themselves. The designer in MetaSPICE, instead of defining multiple SPICE netlists, has to define a single SPICE template that is used to generate multiple netlists. The MetaSPICE is complementary to classical SPICE simulators, and enables the design exploration impact on PUFs' performance. The framework consists of a template engine for generating SPICE models from SPICE templates, and software for evaluating the performance of PUF circuits in terms of uniqueness, uniformity and reliability from SPICE simulation results. We demonstrate the effectiveness of MetaSPICE by evaluating the performance and presenting improvements on a recently proposed PUF design. © 2023 IEEE.

关键词： SPICE

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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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Exploratory Testing of Software Product Linesusing Distance Metrics

Exploratory Testing of Software Product Linesusing Distance ...

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2023 International Conference on Computational Intelligence and Sustainable engineering Solution, CISES 2023

作者： Saini, Ashish Kumar, Satendra Kumar, Raj Kumar, Mukesh Kumari, Amrita Quantum University Dept. of Computer Science and Engineering Roorkee India Moradabad Institute of Technology Dept. of Computer Science and Engineering Moradabad India Gurukula Kangri Deemed to Be University Dept. of Computer Science Haridwar India Quantum University Dept. of Mechanical Engineering Roorkee India

ISBN: (纸本)9798350323917

Quality is very important in any software product, so that the customer trusts the efficiency of the product. The quality or validity of a product is measured by its testing. The test measures the quality of a product as well as shows the validity of the product. For example, testing and measuring the validity of a product in a single software system is not easy. Similarly, the software product line, which produces many different products of the same family at once, makes it even more difficult to check their validity and quality. Several techniques and models have been proposed to test the validity of the software line products and to ascertain the quality. In the same direction, we also propose a method for testing the products of software product line. The proposed method isbased on the distance between the features of the product (Desired and Original). We have used the proposed technique on product line with less features and found that the method presented gives fast and better results than traditional methods. © 2023 IEEE.

关键词： Software testing

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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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Enhancing Multi-label text classification using adaptive promptify concepts 15

Enhancing Multi-label text classification using adaptive pro...

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

作者： Reddy, Veerababu Uppukonda, Usha Rani Veeranjaneyulu, N. Vignan's Foundation for Science Technology & Research Dept. of Computer Science & Engineering Guntur India Vignan's Lara Institute of Technology & Science College Dept. of Computer Science & Engineering Guntur India Vignan's Foundation for Science Technology & Research Dept. of Information Technology Guntur India

ISBN: (纸本)9798350370249

In present days, Multi Label Text Classification (MLTC) has become an important research area in Natural Language Processing (NLP). Existing models of MLTC have been facing domain specificity and fine-tuning challenges. Hence a model is developed to overcome the above said challenges by introducing Prompt engineering, GPT FineTuning methods and Pipeline algorithms. The experimental results are obtained based on the present Generative pre trained data produces labels for various text documents and files with an exceptional accuracy of 85%. The outcomes of the experiments reveal notable enhancements over the baseline models. © 2024 IEEE.

关键词： Fine-Tuning GPT Multi-Label Text classification NLP (Natural Language Processing) Pre-trained models Prompt engineering Transformers

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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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Active Element Arrangement and Prediction Model for Channel Estimation in IRS-Assisted Systems

Active Element Arrangement and Prediction Model for Channel ...

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2024 IEEE Region 10 Conference, TENCON 2024

作者： Tsuchiya, Yoshihiko Suga, Norisato Uruma, Kazunori Fujisawa, Masaya Tokyo University of Science Dept. of Information and Computer Technology Tokyo Japan Shibaura Institute of Technology Dept. of Information and Communication Engineering Tokyo Japan Kogakuin University Dept. of Computer Science Tokyo Japan

ISBN: (纸本)9798350350821

Intelligent reflecting surfaces (IRS) that can dynamically control the phase of radio waves and reflect them are attracting attention to realize non line-of-sight communication in the high-frequency band. Channel state information (CSI) is estimated to properly control the phase, however the overhead of the estimation reduces communication efficiency. To reduce the overhead, IRS systems with active elements that perform channel estimation are considered. One approach treats the partial CSI obtained from the active elements as an image and utilizes a super-resolution technique to predict the entire CSI. However, because the active elements are arranged in grid, prediction is difficult when the active elements spacing and the CSI fluctuation period match. In this study, we propose a new arrangement that gathers the active elements in the center of the IRS. Furthermore, we regard prediction as an image-inpainting problem that recovers missing pixels from known pixel values, and we propose applying the U-Net to solve this problem. Numerical experiments show that the proposed method outperforms conventional methods in prediction accuracy and spectral efficiency. © 2024 IEEE.

关键词： Channel estimation

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