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Exploring Predictive Models for Stress Detection: A Machine Learning Approach 5

Exploring Predictive Models for Stress Detection: A Machine ...

引用

5th International Conference on Image Processing and Capsule Networks, ICIPCN 2024

作者： Kalai Vani, V. Ghouse, Fathima Adhiyamaan College of Engineering Department of Computer Science and Engineering Tamilnadu Krishnagiri India

ISBN: (纸本)9798350367171

The world today is full of conflicts. Furthermore, the intensity and impact of stress can vary widely from person to person, influenced by factors such as personality, coping skills, and overall mental and physical well-being. A questionnaire-based approach is used, where users answer a series of questions about their experiences and feelings. While applying machine learning algorithms to analyze collected data for stress levels can provide valuable insights, it's crucial to consider ethical considerations, privacy concerns, and the need for a holistic approach that incorporates both quantitative data and qualitative understanding of an individual's experiences. Additionally, the classification task is to determine if people are currently suffering from stress. However, it's important to approach the implementation of such interventions with caution, ensuring that ethical guidelines are followed, and prioritizing individual privacy and consent in order to strike a balance between technological advancements and ethical considerations. © 2024 IEEE.

关键词： Adversarial machine learning

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Agricultural Crop Recommendations Based on Productivity and Season 10

Agricultural Crop Recommendations Based on Productivity and ...

引用

10th International Conference on Advanced Computing and Communication Systems, ICACCS 2024

作者： Anandaraj, A. Ram Deepak, P. Rahul Raj, R. Sanjay, R. Sri Eshwar college of Engineering Department of Computer Science and Engineering Coimbatore India

ISBN: (数字)9798350384369

ISBN: (纸本)9798350384369

Tamil Nadu, a coastal state, faces uncertainty in agricultural productivity. With more people and area available, increasing output should be possible, however, it is not. Farmers once relied on word of mouth, but current environmental conditions make that impossible. Agricultural metrics and environmental factors produce the data that is used to get insights into the Agri-facts. In order to give farmers accurate agricultural information, the IT sector's growth has resulted in several improvements in agriculture sciences. It is desirable in the current environment to be able to use modern technology in agriculture. Making predictions and building a precise model from the data is possible with the use of machine learning techniques. Agricultural problems like crop forecasting, crop rotation, and crop protection and water fertilizer needs are solvable. Due to the constantly changing seasons, it is essential to use a systematic approach to make crop growing easier and to support farmers in their farming operations and administration. Aspiring farmers could be able to use better agricultural techniques as a result. A farmer might receive a system of recommendations to help with crop output by means of data mining. Depending on their quantity and weather conditions, crops are recommended for usage in this method. © 2024 IEEE.

关键词： Agribusiness

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Intelligent Beauty Product Recommendation Using Deep Learning 1

Intelligent Beauty Product Recommendation Using Deep Learnin...

引用

1st International Conference on Cognitive, Green and Ubiquitous Computing, IC-CGU 2024

作者： Hanchinal, Tainiyat K Bhavani, Vaishali D Mindolli, Veena B Jain College Of Engineering And Research Department of Computer Science and Engineering Belgaum India

ISBN: (纸本)9798350361780

Skin care is a vital component of one's health and well-being, and there are many products on the market that report different skin problems. People's methods for skin issues have changed dramatically as a result of technological revolutions in the skin care industry and artificial intelligence (AI). The traditional approach of navigating multiple brand websites and overwhelming product choices has prompted the need for a more streamlined and intelligent solution. The AI-driven web application system is proposed to suggest appropriate skin care items depending on a particular skin type. It is an innovative system that brings product recommendations from several brands and websites, as well as cross-platform price comparisons, together in a single, customized area. It also provides a link to the particular platform so that customers may purchase products based on the quality and quantity of products. By using deep learning, which uses convolutional neural networks (CNNs) to understand complex patterns within skin data, users provide the system with an image along with additional information about their skin type, worries, and preferences, and help the system understand and recover appropriate data. The proposed system verifies user knowledge before and after system use, while experts examine rules and designs. By analyzing this data, the deep learning algorithm is able to recognize different skin outlines and certain disorders such as pigmentation, age, dryness, sensitivity, or acne. Also, a trained dataset is used to check for the training and validation accuracy of the system. © 2024 IEEE.

关键词： Deep learning

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Classification of Different Stages of Eczema Disease using YOLOv8 8

Classification of Different Stages of Eczema Disease using Y...

引用

8th International Conference on Electronics, Communication and Aerospace Technology, ICECA 2024

作者： Ezhilin Freeda, S. Hariharan, T. Dinesh, R. Muthuaravind, M. Sri Ramakrishna Engineering College Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350367904

Skin diseases pose significant challenges in both diagnosis and treatment due to their diverse manifestations and complexities. In recent years, the advancement of deep learning techniques has revolutionized medical image analysis, offering promising avenues for automated detection and categorization of dermatological conditions. The primary focus of this system is the detection of different stages of the skin disease 'Eczema' using YOLO model and will be integrated with mobile application. Our methodology entails training the YOLOv8 model on a dataset encompassing 3000 diverse instances of Eczema at various stages. The model undergoes fine-tuning to discern distinctive visual patterns corresponding to different phases of the skin condition. Subsequently, the trained YOLOv8 model is deployed for real-time inference on new images, enabling prompt and reliable identification of Eczema stages. Post-collection, the dataset undergoes labelling to categorically define the various stages of eczema. Performance evaluation of this model demonstrates a commendable accuracy rate of approximately 81.9%, reflecting its robustness in identifying different stages of Eczema. © 2024 IEEE.

关键词： Deep learning

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Spectrogram Features and Xception Classifier Based Audio Spoof Detection System

Spectrogram Features and Xception Classifier Based Audio Spo...

引用

2024 International Conference on computer, Electronics, electrical engineering and their Applications, IC2E3 2024

作者： Chakravarty, Nidhi Dua, Mohit Thapar Institute of Engineering and Technology Computer Science and Engineering Department Patiala India National Institute of Technology Department of Computer Engineering Haryana Kurukshetra India

ISBN: (纸本)9798350388534

Voice biometric authentication has gained significant attention in recent years due to its non-intrusive and user-friendly nature. In this research paper, we present a comprehensive study on the effectiveness of three different spectrograms - Constant Q Transform (CQT), Mel and Gammatone at the front end of an Audio Spoof Detection (ASD) system. The back end of the system employs the Xception pretrained model. Our experimental results reveal that Gammatone Spectrograms outperform both CQT Spectrograms and Mel Spectrograms in terms of performance. Specifically, combination of Gammatone Spectrogram with Xception pretrained has achieved a remarkable 3.5% and 2% Equal Error Rate (EER) for ASVspoof 2021 DF and DECRO dataset, respectively © 2024 IEEE.

关键词： Spectrographs

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Improving Breast Cancer Detection Accuracy Through Random Forest Machine Learning Algorithm

Improving Breast Cancer Detection Accuracy Through Random Fo...

引用

International Conference on Advanced engineering, Technology and Applications, ICAETA 2024

作者： Agrawal, Atul Khanday, Akib MohiUd Din Alazzawi, Esraa Mohammed Bhushan, Bharat Baniya, Pashupati Jamil, Akhtar Department of Computer Science and Engineering ITS Engineering College Greater Noida India Department of Computer Science and Engineering Sharda University Greater Noida India Department of Computer Science and Software Engineering United Arab Emirates University Al Ain Abu Dhabi United Arab Emirates Department of Computer Engineering University of Baghdad Al-Jadriya Baghdad10071 Iraq Department of Computer Science National University of Computer and Emerging Sciences Islamabad44000 Pakistan

ISBN: (纸本)9783031709234

Breast cancer continues to be a significant global health issue that greatly affects the well-being of people worldwide. Detecting breast cancer early is vital for improving the outcomes of patients. One promising method for breast cancer detection is the use of the Random Forest machine learning algorithm. In a recent research paper, we investigated how Random Forest can be used to predict breast cancer by employing explainable AI techniques. We analyzed the specific features that the algorithm relies on to classify breast cancer and highlighted the advantages of Random Forest compared to other machine learning algorithms in diagnosing breast cancer. Our model offers a well-thought-out, efficient, and easily understandable approach to predicting breast cancer using explainable machine learning techniques. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Adversarial machine learning

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Graph Retrieval-Augmented Generation for Large Language Models: A Survey

Graph Retrieval-Augmented Generation for Large Language Mode...

引用

2024 IEEE International Conferences of AI, science, engineering, and Technology, AIxSET 2024

作者： Procko, Tyler Thomas Ochoa, Omar Embry-Riddle Aeronautical University Department of Electrical Engineering and Computer Science Daytona Beach United States

ISBN: (纸本)9798350390995

Large Language Models (LLMs) demonstrate general knowledge, but they suffer when specifically needed knowledge is not present in their training set. Two approaches to ameliorating this, without retraining, are 1) prompt engineering and 2) Retrieval-Augmented Generation (RAG). RAG is a form of prompt engineering, insofar as relevant lexical snippets retrieved from RAG corpora are vectorized and aggregated with prompts. However, RAG documents are often noisy, i.e., while relevant to a given prompt, they can contain much other information that obfuscates the desired snippet. If the purpose of pretraining a LLM on massive and general corpora is to engender a generally applicable model, RAG is not: it is a means of LLM optimization, and as such, RAG document selection must be precise, not general. For expert tasks, it is imperative that a RAG corpus be as noise-free as possible, in much the same way a good prompt should be free of irrelevant text. Knowledge Graphs (KGs) provide a concise means of representing domain knowledge free of noisy information. This paper surveys work incorporating KGs with LLM RAG, intending to equip scientists with a better understanding of this novel research area for future work. © 2024 IEEE.

关键词： Knowledge graph

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Teaching Assistants as Assessors: An Experience Based Narrative 16

Teaching Assistants as Assessors: An Experience Based Narrat...

引用

16th International Conference on computer Supported Education, CSEDU 2024

作者： Ahmed, Faizan Bouali, Nacir Gerhold, Marcus Faculty of Electrical Engineering Mathematics and Computer Science University of Twente Enschede Netherlands

ISBN: (纸本)9789897586972

This study explores the role of teaching assistants (TAs) as assessors in a university’s computer science program. It examines the challenges and implications of TAs in grading, with a focus on their expertise and grading consistency. The paper analyzes grading experiences in various exam settings and investigates the impact on assessment quality. We adopt an empirical methodology and answer the research question by analyzing the data from two exams. The chosen exams have similar learning objectives but they differ in how TAs graded them, thus providing an opportunity to reflect on different grading styles. It concludes with recommendations for enhancing TA grading effectiveness, emphasizing the need for detailed rubrics, training, and monitoring to ensure fair and reliable assessment in higher education. Copyright © 2024 by SCITEPRESS – science and Technology Publications, Lda.

关键词： Grading

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Empowering Rural Communities through Intelligent Surveillance for Suspicious Activity Detection

Empowering Rural Communities through Intelligent Surveillanc...

引用

2024 International Conference on Communication, Computing and Internet of Things, IC3IoT 2024

作者： Lalitha, V. Manimala, G. Magesh, K. Youkesh, G. Selvanarayanan, A. Sri Sairam Engineering College Department Of Computer Science And Engineering Chennai India

ISBN: (纸本)9798350352689

The development of a system for a surveillance aimed at mitigating the escalating issue of crime in rural communities. Rural areas, constituting a significant portion of the world, continue to grapple with inadequate security measures, leading to increased criminal activities. Our proposed system offers a comprehensive solution by detecting and preventing abnormal activities, including theft, robbery, and harassment, in real-time. Additionally, it analyzes previously captured footage, thus providing both proactive and retrospective security. An essential feature of our system is its capability to promptly alert nearby law enforcement agencies and individuals connected to potential victims. By implementing this intelligent surveillance system, we aim to bolster the safety and security of rural communities, fostering sustainability and peace. This innovative solution reduces opportunities for criminal activities, enhances response times, and offers crucial evidence for investigations and prosecutions. Our research showcases the practicality and effectiveness of this system in addressing the glaring security gaps prevalent in rural areas, ultimately improving the quality of life for rural residents. © 2024 IEEE.

关键词： Crime

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Improving Machine Learning Algorithm’s Accuracy for Detecting Code Smell Using Data Balancing and Parameter Optimization 5th

Improving Machine Learning Algorithm’s Accuracy for Detecti...

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5th International Conference on Computing, Communications, and Cyber Security, IC4S 2023

作者： Nandini, Abhishilpa Singh, Randeep Rathee, Amit Department of Computer Science and Engineering IEC University Solan India Department of Computer Science GC Barota Haryana Sonipat India

ISBN: (纸本)9789819773701

Detecting code smells is vital for improving software quality, improving its maintainability, and reducing software failures and faults. Code smell detection using machine learning-based methods has become more prevalent in recent years, as they easily overcome the limitations of heuristic-based methods that are based mostly on rules that must be manually created. Data imbalance and parameter optimization are found to be directly affecting performance of underlying machine learning models. However, to the best of our knowledge about literature of the field, little experimental evaluation is carried out in the direction of impact of data balancing and parameter tuning on machine learning models. Therefore, this paper considers it important to propose a new data balancing and parameter optimization technique. Further, the proposed approach is experimentally validated by considering standard code smell datasets. The empirical evaluation considers four code smells taken from two categories (two at method level and rest of the two are considered at class level) and studied four machine learning models, viz., Decision Tree, Random Forest, kNN, and SVM. The obtained experimental results justify the suitability of proposed data balancing and parameter optimization techniques in improving performance of machine learning models. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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