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Breaking Barriers: Investigating the Sense of Belonging Among Women and Non-Binary Students in Software engineering 46

Breaking Barriers: Investigating the Sense of Belonging Amon...

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46th International Conference on Software engineering: Software engineering Education and Training, ICSE-SEET 2024

作者： Boman, Lina Andersson, Jonatan de Oliveira Neto, Francisco Gomes University of Gothenburg Dept. of Computer Science and Engineering Gothenburg Sweden Chalmers and the University of Gothenburg Dept. of Computer Science and Engineering Gothenburg Sweden

ISBN: (纸本)9798400704987

Women in computing were among the first programmers in the early 20th century and were substantial contributors to the industry. Today, men dominate the software engineering industry. Research and data show that women are far less likely to pursue a career in this industry, and those that do are less likely than men to stay in it. Reasons for women and other underrepresented minorities to leave the industry are a lack of opportunities for growth and advancement, unfair treatment and workplace culture. This research explores how the potential to cultivate or uphold an industry unfavourable to women and non-binary individuals manifests in software engineering education at the university level. For this purpose, the study includes surveys and interviews. We use gender name perception as a survey instrument, and the results show small differences in perceptions of software engineering students based on their gender. Particularly, the survey respondents anchor the values of the male software engineer (Hans) to a variety of technical and non-technical skills, while the same description for a female software engineer (Hanna) is anchored mainly by her managerial skills. With interviews with women and non-binary students, we gain insight on the main barriers to their sense of ambient belonging. The collected data shows that some known barriers from the literature such as tokenism, and stereotype threat, do still exist. However, we find positive factors such as role models and encouragement that strengthen the sense of belonging among these students. © 2024 Copyright held by the owner/author(s).

关键词： Students

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Automated Identification of Pneumonia Employing Convolution Neural Networks and the VGG-16 Model 1

Automated Identification of Pneumonia Employing Convolution ...

引用

1st International Conference on Computing, sciences and Communications, ICCSC 2024

作者： Srivastava, Anamika Gupta, Anuj Kaushik, Deepti Gupta, Neha Chadha, Shikha Chauhan, Rosey Noida Institute Of Engineering Technology Dept. of Computer Science & Engineering Gerater Noida India Sharda Unviersity Dept. of Computer Science & Engineering Gerater Noida India School of Engineering and Technology IIMT University Dept. of Computer Science and Engineering U.P Meerut India ABES Engineering College Dept. of Computer Science UP Ghaziabad India

ISBN: (纸本)9798350353648

Pneumonia, an inflammatory lung disease, impacts the small air sacs known as alveoli and is typically caused by the bacteria Streptococcus pneumonia. Symptoms commonly include a combination of productive or dry cough, chest pains, difficulty breathing, and fever. In India, where Covid-19 is prevalent, community-acquired pneumonia (CAP) poses a significant risk factor for mortality, accounting for one-third of fatalities, as reported by the World Health Organization (WHO).Diagnosing and predicting the outcome of pneumonia typically involves examining chest imaging radiographs of patients exhibiting clinically similar symptoms (e.g., fever, dyspnea, cough, and sputum production). Therefore, the development of an automated method for pneumonia detection could offer valuable advantages, particularly in remote regions, facilitating prompt treatment. Deep learning (DL) models enable the analysis of various chest radiographs, with Convolutional Neural Networks (CNNs) receiving considerable attention in disease classification. Leveraging features learned. © 2024 IEEE.

关键词： Convolutional neural networks

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Automatic Segmentation and Categorization of Liver Tumors in CT Scans: A Bibliometric Analysis 3

Automatic Segmentation and Categorization of Liver Tumors in...

引用

3rd International Conference for Innovation in Technology, INOCON 2024

作者： Vijayalakshmi, B. Abraham, Jobin John Subrahmanyan, D. Rajalakshmi Engineering College Dept of Computer Science Engineering Chennai India

ISBN: (纸本)9798350381931

Liver tumors rank as the third deadliest cancer globally and the sixth most prevalent disease worldwide. They primarily afflict individuals who frequently consume tobacco or alcohol. Approximately 75-85 percent of primary liver cancer cases are attributed to these factors. However, manually diagnosing liver tumors presents significant challenges due to tumor heterogeneity, varying shapes and sizes, imaging artifacts, and limited annotated data. The critical task of segmenting liver tumors in medical images greatly impacts diagnosis and treatment planning. Numerous techniques and frameworks have been formulated for the early identification of tumors, improving accuracy, and aiding doctors in understanding tumor characteristics such as size and volume. Nevertheless, this process is error-prone and cumbersome. To address these challenges, a proper model is needed to identify the tumor and used for real-time image processing. This paper motivates to develop a system and assists doctors in swiftly identifying and segmenting tumors from images, providing an approximation of tumor size and stage for more precise treatment. Manual diagnosis is challenging, but advanced DL methods like TransUNet help with early detection and tumor segmentation, assisting doctors in providing timely and accurate treatment. © 2024 IEEE.

关键词： Tumors

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Audio-Music Fingerprinting Recognition 3

Audio-Music Fingerprinting Recognition

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3rd International Conference on Pervasive Computing and Social Networking, ICPCSN 2023

作者： Girisha, G.S. Murthy, Chinmaya Chirayu, S.M. Kavalli, Dayanand Divya, J. Dayananda Sagar University Dept.of Cse Bengaluru India Dayananda Sagar University Computer Science Engineering Bengaluru India

ISBN: (纸本)9798350322842

Audio fingerprinting is the method involved with addressing a sound sign minimally with the aid of isolating vital highlights of the sound substance a part of the good sized makes use of of acoustic fingerprinting includes substance-based sound healing broadcast watching and so forth it lets in gazing the sound free of its arrangement and with out the requirement for metadata it really works by using studying frequency styles and tracking down a fit internal its statistics set of tunes this utility tries to understand the songs through the use of a time-frequency graph primarily based on an audio fingerprint that is known as a spectrogram the software program utilizes a cell phone implicit microphone that assembles a concise instance of a legitimate that is played it analyzes the outside sound and seeks a comparable suit on a database in which thousands and thousands of songs are saved based totally on an acoustic fingerprint when the software reveals a in shape it retrieves records such as the album track name original music and so forth. © 2023 IEEE.

关键词： Audio acoustics

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Plagiarism Detection and Similarity Checking Program using Machine Learning and String Matching Algorithm 9

Plagiarism Detection and Similarity Checking Program using M...

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9th International Conference on Communication and Electronics Systems, ICCES 2024

作者： Bhavana, Munugapati Rao, Koppula Srinivas Koduru, Gagan Kumar Vatsal, K.Vijay Krupa Parvathi, S. Sravani, Mogiligidda Mlr Institute of Technology Affiliated to Jntu Dept. of Computer Science & Information Technology Hyderabad India Mlr Institute of Technology Affiliated to Jntu Department of Computer Science & Engineering Hyderabad India Mlr Institute of Technology Hyderabad India Malla Reddy University Department of Computer Science & Engineering Hyderabad India Mlr Institute of Technology Dept.of Computer Science & Information Technology Hyderabad India Mvsr Engineering College Department of Information Technology India

ISBN: (纸本)9798350377972

This research proposes a system for detecting plagiarism in academic submissions. The system utilizes advanced text comparison algorithms to identify instances of plagiarism, ensuring academic integrity and promoting a culture of originality among students. By providing educators with a tool to efficiently detect plagiarism, this system aims to streamline the evaluation process and enhance the overall learning experience for students. This system has the potential to significantly reduce plagiarism incidents and foster a more ethical and productive academic environment. © 2024 IEEE.

关键词： Contrastive Learning

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Access Point Management Using Java Based Microservice 8

Access Point Management Using Java Based Microservice

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8th IEEE International Conference on Computational System and Information Technology for Sustainable Solutions, CSITSS 2024

作者： Bhat, Manoj Vishalakshi Prabhu, H. Rv College of Engineering Dept of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798331505462

This research paper explores the design and implementation of an event-driven monitoring system for Wi-Fi6 Access Points (APs) using Java-based microservices architecture. WiFi 6's primary advantage over other standards is that it provides maximum speed and accessibility in congested networks. The system aims to enhance network management capabilities by leveraging real-time event processing and Kafka messaging infrastructure. Key components include event listeners for capturing Quality of Experience (QoE) and Trap events via Kafka topics from APs, microservices for event processing, and Kafka topics for inter-service communication. The paper discusses the architecture, functionality, and integration of each component, as well as the system's ability to identify and handle various event types, such as ap-info and user reports. Additionally, the paper addresses error handling mechanisms and debugging practices for events with undefined syntax. Overall, the research contributes to the advancement of network monitoring solutions through the utilization of microservices and events-driven architectures. © 2024 IEEE.

关键词： Wi-Fi

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A Transformer-Based Model for Image Caption Generation with Memory Enhancement

A Transformer-Based Model for Image Caption Generation with ...

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2024 IEEE International Women in engineering (WIE) Conference on Electrical and computer engineering, WIECON-ECE 2024

作者： Ome, Shadika Afroze Azhar, Tanvir Asaduzzaman East Delta University Dept. of Computer Science & Engineering Chattogram Bangladesh Chittagong University of Engineering & Technology Dept. of Computer Science & Engineering Chattogram Bangladesh

ISBN: (纸本)9798331535476

This study proposes a convolution-free transformer-based method for generating accurate descriptions of images. A Vision Transformer is utilized as the primary encoder, replacing traditional CNN, and a Meshed Memory Transformer is integrated for generating captions. The Vision Transformer splits images into fixed-size patches for extracting visual features, which are further processed by a memory-augmented encoder to integrate prior knowledge. It ensures key features are preserved and reused and the meshed decoder combines the text embedding with the outputs from each encoding layer to generate more contextually rich and accurate captions compared to conventional approaches. The model's performance is evaluated on the Flickr30K and the MSCOCO datasets, demonstrating that the Meshed Memory architecture in our approach improves both the image encoding and caption generation step. Our model outperforms the conventional 'CNN-LSTM' models and 'CNN-Transformer' methods, achieving a 1.02% improvement in METEOR and 1.31% in SPICE on the MSCOCO dataset. Additionally, on the Flickr30k dataset, our approach exhibits superior performance across most metrics, notably a 6.37% increase in the METEOR score. © 2024 IEEE.

关键词： Encoding (symbols)

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A Comprehensive Analysis on Skin Cancer Classification Using Transfer Learning 3

A Comprehensive Analysis on Skin Cancer Classification Using...

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3rd International Conference on Advancement in Electrical and Electronic engineering, ICAEEE 2024

作者： Renu, Israt Zerin Haque, Md. Momenul Paul, Subrata Kumer Mou, Mahmuda Shirin Rahman, Md Nahid Gupta, Sudipta Sen Paul, Rakhi Rani Rabindra Maitree University Dept. of Computer Science and Engineering Kushtia7000 Bangladesh Qadirabad Dept. of Computer Science and Engineering Dayarampur Natore6431 Bangladesh University of Rajshahi Dept. of Computer Science and Engineering Rajshahi6205 Bangladesh

ISBN: (纸本)9798350388282

Skin cancer, a prevalent and potentially life-threatening disease, necessitates accurate and timely diagnosis for effective treatment. In addressing this critical healthcare challenge, this research proposes a novel approach to skin cancer classification. The study identifies the need for enhanced diagnostic accuracy and introduces a solution by integrating the efficiency of transfer learning, specifically utilizing the EfficientNet V2 architecture. The proposed methodology leverages pre-trained convolutional neural networks (CNNs) to extract high-level features from dermatological images, which are subsequently input into a sophisticated classifier, forming a robust framework for precise skin cancer classification. The research employs a diverse dataset comprising 3297 images sourced from the publicly available Skin Cancer ISIC archive dataset, ensuring comprehensive coverage of benign and malignant skin lesions. During the training phase, the proposed EfficientNet V2 transfer learning approach outperforms conventional methods, achieving an impressive accuracy of 84%, surpassing both Inception V3 (82%) and a generic CNN model (81%). Interpretability analysis is conducted to elucidate the decision-making process of the EfficientNet V2 model, providing insights into the key features influencing its predictions. This transparency is crucial for building trust in the model's recommendations and facilitating its seamless integration into clinical workflows. The EfficientNet V2 transfer learning approach emerges as a promising and effective solution for advancing skin cancer classification systems, contributing significantly to the ongoing efforts to improve diagnostic accuracy in dermatology. © 2024 IEEE.

关键词： Decision making

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Mental Health Harmony: Insights from the Machine Learning Frontier 3

Mental Health Harmony: Insights from the Machine Learning Fr...

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3rd International Conference on Smart Technologies and Systems for Next Generation Computing, ICSTSN 2024

作者： Nithiyasree, P. Subramani, Kavitha Soorya, S. Arundhathi, T. Harshitha, Vatturi Panimalar Engineering College Dept of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350391565

Detecting mental health problems at the earliest can help professionals provide correct treatment and increase the value of patients. One of the most prevalent types of impairment in the world is depression. It is important to deal adolescent mental health issues which can escalate to more serious problems if not addressed early. Machine learning techniques are ideal for estimating and diagnosing medical data. Feature selection techniques have been applied to the entire attribute dataset to reduce attributes. Machine Learning accuracy is associated with attributes from the dataset. Three algorithms are employed in this work, Logistic Regression, Random Forest Classifiers, and Decision Tree Classifiers, in data science techniques. Sensitivity and Specificity are measures that estimate a model's capability to predict true positive and negative results. When these algorithms are compared the random forest classifier yields 95% accuracy when compared with other algorithms. © 2024 IEEE.

关键词： Adversarial machine learning

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An Effective Analysis of Machine Learning Models for Prediction of Heart Disease 2

An Effective Analysis of Machine Learning Models for Predict...

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

作者： Patil, Rudragoud Kamkar, Shambahvi C. Goudar, Shwetha Indudhar K.L.S.Gogte Institute of Technology Dept of Computer Science and Engineering Karnataka Belagavi India K.L.S Gogte Institute of Technology Dept.of Master of Computer Applications Karnataka Belagavi India

ISBN: (纸本)9798350372892

Heart failure is a chronic illness that impacts millions of individuals worldwide. In order to forecast heart failure health status early and take the necessary activities to address this global issue an effective machine learning-based technique is required. Globally, heart disease is one of the leading causes of death. Medical professionals find it challenging to forecast since it demands experience and a deeper level of predictive knowledge. The healthcare industry operates in a knowledge-deficient but information-rich environment. Healthcare systems have access to a vast amount of data via the internet, but there aren't many useful tools for analyzing the data to find hidden patterns. Medical efficiency will increase with an automated system, which will also save money and time. This software uses information acquired from Kaggle to forecast the likelihood of a disease developing. To create a prediction model, the following algorithms are used: Random Forest (RF), support vector machine (SVM), K-nearest neighbor (KNN), Decision Tree (DT), and Logistic Regression (LR). Phases for feature selection and data preprocessing were finished before the models were created. When the models were evaluated based on accuracy and the F1-score, the RF model performed the best. © 2024 IEEE.

关键词： Adversarial machine learning

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