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Breast Cancer Detection using image segmentation and Machine Learning approaches

Breast Cancer Detection using image segmentation and Machine...

引用

2023 Annual International Conference on Emerging Research Areas: International Conference on Intelligent Systems, AICERA/ICIS 2023

作者： Gowd, Nadavadi Harshith Karthikeya, Pavan Sneha Snigdha, S.V. Hilda, J.Jabanjalin School of Computer Science and Engineering Vellore Institute of Technology Vellore India

ISBN: (纸本)9798350303452

The most prevalent cancer in women worldwide is breast cancer. A better outlook and lower mortality rates depend on early detection. Machine learning algorithms have recently demonstrated encouraging results in assisting breast cancer detection. This abstract seeks to give a summary of the research on machine learning algorithms for breast cancer detection. Various machine learning methods, such as artificial neural networks, support vector machines, and random forests, among others, have been used in a number of studies. Mammography image and patients data are used to train these algorithms, which can reliably predict the presence of breast cancer. The findings of these studies suggest that machine learning methods can support the highly accurate, sensitive, and specific detection of breast cancer. These algorithms can also recognise features and trends. Some algorithms might perform well on specific datasets but struggle to generalize to different populations or diverse patient demographics. This can limit their applicability in real-world scenarios. Image segmentation methods are employed to isolate the region of interest (ROI) containing potential abnormalities. To identify the key-points we compared two feature extraction algorithms SIFT (Scale-Invariant Feature Transform), ORB (Oriented FAST and Rotated BRIEF) © 2023 IEEE.

关键词： Image segmentation

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An AgroBot: Natural Language Processing Based Chatbot for Farmers 4

An AgroBot: Natural Language Processing Based Chatbot for Fa...

引用

4th International Conference on Smart Electronics and Communication, ICOSEC 2023

作者： Marla, Anushka Paul, Rejath Saha, Arupam Kumar Basha, Niha Kamal Balasundaram, Anandhakrishnan School of Computer Science and Engineering Vellore Institute of Technology Vellore India

ISBN: (纸本)9798350300888

In an industry that is enduring a rapid transformation, rowers are gaining access to new technologies that help them improve agricultural yields and resource management. TensorFlow-built chatbots can provide instantaneous assistance to producers. TensorFlow is a machine learning (ML) framework for data automation, model monitoring, and model retraining. It was used to analyse more than 80 million call records from the government portal Kisan Call Canter (KCC) using multiple data preprocessing techniques in conjunction with TensorFlow. Training enabled the chatbot to distinguish between agricultural questions, crop management, soil health, and pest control intentions and entities. This chatbot intends to implement a voice recognition module that will enable users to submit speech-based queries rather than text-based ones. In the future, the chatbot can be trained to comprehend images with high precision and be able to identify crops, provide recommendations on how to increase crop productivity, and promote the need for sustainable farming. The automaton has been evaluated using metrics for precision and error reduction. It's conceivable that TensorFlow-trained chatbots could revolutionize agriculture by providing producers with real-time data and advice on how to increase their yields and revenue. © 2023 IEEE.

关键词： Crops

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Music Genre Classification Using Federated Learning

Music Genre Classification Using Federated Learning

引用

World Conference on Information Systems for Business Management, ISBM 2022

作者： Gupta, Lakshya Meedinti, Gowri Namratha Popat, Anannya Perumal, Boominathan School of Computer Science and Engineering Vellore Institute Of Technology Vellore India

ISBN: (纸本)9789811974465

Federated learning (FL) is a decentralized privacy-preserving machine learning technique that allows models to be trained using input from multiple clients without requiring each client to send all of their data to a central server. In audio, FL and other privacy-preserving approaches have received comparatively little attention. A federated approach is implemented to preserve the copyright claims in the music industry and for music corporations to ensure discretion while using their sensitive data for training purposes in large-scale collaborative machine learning projects. We use audio from the GTZAN dataset to study the use of FL for the music genre classification task in this paper using convolutional neural networks. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Convolution

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Leveraging RAG for Effective Prompt engineering in Job Portals 2

Leveraging RAG for Effective Prompt Engineering in Job Porta...

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2nd International Conference on Computational Intelligence, Communication technology and Networking, CICTN 2025

作者： Haneef, Farhan Varalakshmi, M. Peer Mohamed, P.U. School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Vellore India CapitalOne United States

ISBN: (纸本)9798331530389

Traditional recruitment methods are time-consuming and heavily reliant on manual processes, leading to inefficiencies. This paper explores the integration of AI technologies into a job portal to streamline the hiring process, focusing on AI's ability to automatically parse and extract information from resumes and job descriptions to match candidates with relevant job opportunities. The platform leverages Large Language Models (LLMs) to extract key information, such as skills, education, and work experience, from both resumes and job descriptions. Fine-tuning through prompt engineering ensures accurate extraction, even when data fields are incomplete or missing. To further enhance matching accuracy, Retrieval-Augmented Generation (RAG) techniques are employed. These mechanisms retrieve relevant information from a structured skills database to provide context, which, combined with the generative capabilities of LLMs, enables more contextually accurate matches. Candidates receive personalized job recommendations based on the information extracted from their resumes, while employers can post job descriptions and use AI-driven tools to match candidates. This approach of using contextual prompts not only improves matching accuracy but also reduces computational time, eliminating the need for custom models tailored specifically to resumes or job descriptions. © 2025 IEEE.

关键词： Employment

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Hyperparameter Optimization of Machine Learning Models for Monitoring Menstrual Mental Health 1

Hyperparameter Optimization of Machine Learning Models for M...

引用

1st IEEE International Conference on Contemporary Computing and Communications, InC4 2023

作者： Chawla, Tisha Mittal, Shubh Srinivasan, Kathiravan Vellore Institute of Technology School of Computer Science and Engineering Vellore India

ISBN: (纸本)9798350335774

Monitoring and tracking mood disorders associated with Menstruation are becoming crucial to avoid impairment in the mental health of a woman, prevent disruption in family life and relationships, and allow women to maintain a proper balance in their work life. Every sentiment linked during the menstruation cycle can be identified for improving fecundability and health management. This study aims to analyze the feelings of women during Premenstrual dysphoric disorder (PMDD) and the actual menstrual cycle using text-mining methods. We used sentimental analysis in a Reddit dataset to identify positive and negative emotions in women. In the proposed framework, public sentiments were first processed and data was balanced using Synthetic Minority Over-sampling Technique (SMOTE) followed by classification using different machine learning frameworks like Support Vector Machine (SVM), Decision Tree Classifier, Logistic Regression, Random Forest, AdaBoost Classifier and Boosting Classifier. The findings of this study exhibit that women evoke both positive and negative sentiments. However, compared to other feelings the negative emotion is at its inception. The result shows that Random Forest with tuned hyperparameters gives us the best accuracy of 94% when compared to other baseline and ensemble machine learning models with different feature extraction techniques. © 2023 IEEE.

关键词： Support vector machines

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Comparative Analysis on Solar Panel Defect Detection Using Deep Learning Approaches

Comparative Analysis on Solar Panel Defect Detection Using D...

引用

2023 International Conference on Data science, Agents and Artificial Intelligence, ICDSAAI 2023

作者： Raja, Viswanadhapalli Rakesh, Varanasi Kumar, Avinash Siva Sankari, S. Vellore Institute of Technology School of Computer Science and Engineering Vellore India

ISBN: (纸本)9798350348910

Solar energy is one of the widely used renewable energy for commercial as well as house hold purposes. Even though solar energy is widely used, still one time installation change for the panel is high. The production of panel is not yet cost effective. In India, several panel manufacturers are trying to bring defect free product into the market. There are several methods adopted to make the panel defect free during manufacture as well as in working condition when installed in solar farms. Artificial Intelligence can be used to tackle the complex problems of designing solar plants and can save diverse power system issues like planning, control, scheduling, forecasting and so on. It can handle the demanding requirements that applications in current big power systems confront so as to meet increasing load demand. Solar farm owners usually used a team of employees to physically check solar panels for faults. This process is time-consuming, not cost effective and also less accurate. Maintenance visit to solar farms is very expensive and are simply not feasible to perform on daily basis for an entire solar development. The inspection process can be speedup by turning into an artificial intelligence powered inspection. It entails using algorithms to detect solar panel defects from photographs, which is quicker and more precise than human inspection. Eventhough artificial intelligence technology helps a lot to find the solar panel defect perfectly, it has some challenges that needs to be overcome. These challenges are addressed by using the deep learning methods like MobileNetV2, ResNetV1 and inceptionv3. The present paper analyses MobileNetV2, ResNetV1 and inceptionv3 for detecting solar panel defect. The simulation result shows MobileNetV2 provides better accuracy compared to others. © 2023 IEEE.

关键词： Solar energy

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A Federated Deep Learning Method for Chronic Disease Diagnosis 4

A Federated Deep Learning Method for Chronic Disease Diagnos...

引用

4th IEEE Global Conference for Advancement in technology, GCAT 2023

作者： Srinidhi, Vaishnav Victor, Akila School of Computer Science and Engineering Vellore Institute of Technology Vellore India

ISBN: (纸本)9798350305258

Deep learning has become increasingly important in the diagnosis of Alzheimer's disease due to its ability to analyze vast amounts of medical data with exceptional accuracy. The goal of this study is to conduct research on computer aided diagnosis to improve the accuracy and efficiency of diagnosing Alzheimer's disease, and to make the diagnosis process more accessible to patients in remote or underserved areas. By training deep neural networks on large datasets, we can identify subtle patterns in patient data that may indicate the presence of Alzheimer's disease. Furthermore, deep learning can help personalize treatment plans and monitor disease progression over time, leading to better patient outcomes. In this study, we shall introduce a novel computer Aided Diagnosis (CAD) algorithm using transfer learning. Keeping security in mind, the models have been trained in a federated environment to ensure that satisfactory results are obtained while maintaining differential privacy. © 2023 IEEE.

关键词： Alzheimer's Disease computer Vision Federated Algorithm Transfer Learning

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Execution Time Analysis of Multithreading and Multiprocessing on Seam Carving Algorithm

Execution Time Analysis of Multithreading and Multiprocessin...

引用

2023 International Conference on Advances in Intelligent Computing and Applications, AICAPS 2023

作者： Joshi, Aditya Deepak Bhattacharyay, Rajarshi Luhadia, Garvit Vijayarajan, V. Vellore Institute of Technology School of Computer Science and Engineering Vellore India

ISBN: (纸本)9798350333817

The objective of this research is to demonstrate the performance difference exhibited by the seam carving algorithm when executed sequentially on a conventional CPU as opposed to when it is run parallelly. Multithreading and multiprocessing were two of the parallelization methods used in this project. These execution times were recorded, and an execution time analysis was performed. In the end, the researchers compared the two to determine which parallelization technique performed better. The researchers believe that the applications of this algorithm when parallelized can be significantly expanded. This expansion could potentially allow it to be used in applications such as thumbnail generation, responsive web development, and a complete contentaware alternative to image resizing (skewing and stretching). © 2023 IEEE.

关键词： Image processing

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DifEvoDenseFed: Differential Evolution Optimization Based DenseNet Federated Model in Alzheimer's detection

DifEvoDenseFed: Differential Evolution Optimization Based De...

引用

2023 IEEE International Conference on Next Generation Electronics, NEleX 2023

作者： Ghosh, Ananya Krishnamoorthy, Parthiban School of Computer Science and Engineering Vellore Institute of Technology Vellore India

ISBN: (纸本)9798350319088

DifEvoDenseFed is introduced as an innovative solution for Alzheimer's disease prediction, leveraging the combined strengths of Differential Evolution Optimization (DifEvo), DenseNet, and Federated Learning (Fed). The urgency of Alzheimer's as a global health concern necessitates accurate early detection for effective intervention. In this advanced model, DenseNet forms the cornerstone, capitalizing on its proficiency in image-based tasks. At the client level, Differential Evolution Optimization (DifEvo) fine-tunes model parameters locally, adapting to the unique characteristics of each dataset. This individualized optimization enhances precision and ensures adaptability across diverse data sources. The collaboration between multiple clients is orchestrated by Federated Learning, preserving data privacy and decentralizing the learning process. Clients retain control of their sensitive medical data, reducing privacy risks associated with centralized systems. This decentralized approach improves scalability and fault tolerance. The synergy of these components culminates in a robust and accurate Alzheimer's prediction model. Local Differential Evolution (DifEvo) optimizations refine parameters, which are aggregated at a central server to iteratively enhance the global model. This process ensures ongoing model improvement while minimizing communication overhead. Moreover, the versatility of this approach extends beyond Alzheimer's prediction, making it suitable for various medical image analysis tasks. It fosters community collaboration among healthcare institutions, promoting a collective effort to combat Alzheimer's disease. It is important to note that the model's effectiveness is contingent upon data quality, system design, and DifEvo's optimization capabilities. Rigorous validation and evaluation are essential to measure its real-world impact. 'DifEvoDenseFed' marks a significant stride in early Alzheimer's detection, privacy-preserving AI in healthcare, and collab

关键词： Forecasting

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Automated Personality-Based Candidate Shortlisting using Machine Learning and Natural Language Processing 2

Automated Personality-Based Candidate Shortlisting using Mac...

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2nd International Conference on Augmented Intelligence and Sustainable Systems, ICAISS 2023

作者： Anusha, K. O. Vedasree Dhar, Akanksha Dixit, Shashwat Saraf, Amay Lonial, Ishaan Anusha, N. Vellore Institute of Technology Computer Science and Engineering Vellore India Vellore Institute of Technology Department of IoT Vellore India

ISBN: (纸本)9798350325799

With the exponential increase in the number of job seekers, the hiring process has become more challenging as it requires assessing both the skills and personality traits of candidates. Manual screening of each CV is time-consuming and inefficient. To address this, the proposed system utilizes Machine Learning algorithms and Natural Language Processing (NLP) techniques to automatically identify the personality traits of candidates through a small test and CV analysis. By gathering data from various sources and extracting relevant features, the system predicts personality traits using trained models. With the proposed system, recruiters can make informed decisions and effectively manage the influx of job applicants, improving the efficiency and effectiveness of the hiring process in the face of the growing number of job seekers. © 2023 IEEE.

关键词： Support vector machines

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