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An Efficient Method For Skin Cancer Detection Using Convolutional Neural Network 2

An Efficient Method For Skin Cancer Detection Using Convolut...

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2nd IEEE International Conference on Advances in Information technology, ICAIT 2024

作者： Rajagopal, R. Karthik Kumar, G.V. Senbagavalli, M. Sai Priya, P. Divya Department of Computer Science and Engineering Alliance College of Engineering and Design Alliance University Bangalore India Department of Computer Science & Information Technology Alliance College of Engineering and Design Alliance University Bangalore India

ISBN: (纸本)9798350383867

Skin cancer poses a significant health risk, often overlooked despite its prevalence. Manual detection by clinicians may sometimes be inadequate, leading to the necessity of automated systems utilizing deep learning. In this study, we propose a computerized solution employing Convolutional Neural Networks (CNNs) to detect skin diseases from imaging data. By leveraging the Kera's Sequential API, we design and train various neural network architectures, analyzing their performances in skin cancer detection. Our custom CNN model achieves an accuracy of approximately 80%. Subsequently, we explore the efficiency of transfer learning models such as VGG11 to improve accuracy and for comparative analysis. Notably, the vgg16 architecture achieves the highest accuracy of 86% making it a competitive solution for skin cancer detection. © 2024 IEEE.

关键词： Convolutional neural networks

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Predicting Employee under Stress Using Machine Learning

Predicting Employee under Stress Using Machine Learning

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2024 IEEE International Conference on Signal Processing and Advance Research in Computing, SPARC 2024

作者： Javeriya, Thonduru Anjum Kabilan, M. Manoj, Golla Sree, Yarragonda Bhavya Jagadish, Rongala Venkata Satya Mir, Mahmood Hussain Debnath, Saswati Girish, B.M. Alliance College of Engineering and Design Karnataka Bengaluru India Alliance College of Engineering and Design Karnataka Bengaluru India Alliance College of Engineering and Design Karnataka Bengaluru India Alliance College of Engineering and Design Department of Computer Science and Information Technology Karnataka Bengaluru India Alliance College of Engineering and Design Department of Mechanical Engineering Karnataka Bengaluru India

ISBN: (纸本)9798350385199

Because of COVID-19, businesses have had to change how they operate, like having people work from home or having fewer employees in the office. This 'new normal' is sticking around for a while. Workers have had to adjust to these changes, which can make them feel tired and Pressure. Stress happens when people feel overwhelmed and uncertain. There are lots of things that can cause stress for workers, like getting paid less, having too much work to do, feeling lonely while working from home, working longer hours, not having a good place to work, not feeling like part of a team, feeling like they can't do their job well, and worrying about getting sick. If bosses can recognize when workers are starting to feel stressed, they can help fix the problem, which can make things better for both the worker and the company. © 2024 IEEE.

关键词： COVID-19

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E-Authentication System using Eye-Blinking and Deep Learning 2

E-Authentication System using Eye-Blinking and Deep Learning

引用

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

作者： Achary, Rathnakar Shelke, Chetan J Alliance University Alliance College of Engineering and Design Department of Computer Science Bangalore India

ISBN: (纸本)9798350372892

The Personal Identification Number (PIN) are commonly employed as a security measure for authentication purposes. When utilizing a PIN for verification of authentication, the users are required to input a physical PIN. However, this method may be susceptive to security vulnerabilities through methods such as observing someone's shoulder or tracking thermal signatures. In this research, we adopted Eye-Blinking for authentication of the system, with the advantage that it leaves no bodily footprints in the system and provides a more robust alternative for advanced security. The eye-blinking pattern is a highly secured and unique biometric identifier that cannot be easy replicates, making it difficult to hack or bypass. An E-Authentication System using Eye-Blinking and its analysis using deep learning explains an individual's eye-blinking patterns as a unique identifier to verify their identities. The systems capture their eye-blinking patterns and compare them to the stored patterns in its databases. If the patterns match, the systems grant access to the user. The systems aim at achieving high accuracy, reliability, and integration with existing security infrastructure and authentication protocols. © 2024 IEEE.

关键词： Authentication Protocol

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Leveraging ML Algorithms to Predict IT Job Opportunities and Inform Programme Approval 5th

Leveraging ML Algorithms to Predict IT Job Opportunities and...

引用

5th International Conference on Computing and Network Communications, CoCoNet 2023

作者： Sudharshana, N. Naren, J. Sumathi, D. Chennai India Department of Computer Science and Engineering Alliance College of Engineering and Design Alliance University Bengaluru India

ISBN: (纸本)9789819747108

Unemployment is a serious concern around the world. Every year, millions of graduates are without a job. Students enrol in graduate schools without understanding what the job market expects. Upon graduation, the necessary skills to secure employment are often lacking. In such instances, it can be inferred that degree programmes fail to prepare students for job applications and interviews. A framework must be in place to educate graduates about available employment. Hence a job recommender system comes into the picture. Its main objective is to recommend jobs to students based on technical skillset, logical quotient rating, self-learning capability and a variety of other criteria. The system can also serve as feedback for programmes as it gauges the employability of the student passing out. It can also be used by regulatory councils (government organizations like AICTE which regulate and approve technical programmes) to decide whether the programme should be continued based on the number of job-ready graduates produced. The proposed work has employed classifier algorithms (machine learning), for categorizing student placement data based on features like ‘Academic Percentage in Operating Systems’, ‘Percentage in computer Networks’, ‘Hours working per day’, etc. The performance of various classifiers is also analyzed. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Students

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Prediction of Student Performance in Online Classes During COVID-19 Pandemic 5th

Prediction of Student Performance in Online Classes During C...

引用

5th International Conference on Computing and Network Communications, CoCoNet 2023

作者： Sudharshana, N. Naren, J. Sumathi, D. Chennai India Department of Computer Science and Engineering Alliance College of Engineering and Design Alliance University Bengaluru India

ISBN: (纸本)9789819747108

Despite global disruptions caused by the COVID-19 pandemic, education sector adapted and continued to function online. Undergraduate courses happened online. Variations in students’ performance emerged due to differing approaches in incorporating online education into routine life amidst challenging conditions at home and in society during the pandemic. It was necessary to find out whether online education aided academics. A solution based on a machine learning is proposed and implemented in the paper to forecast an individual’s performance using data gathered from a user survey among university students regarding their perceptions of online education. The user survey comprised questions about factors that could affect academic performance. A diverse range of algorithms are meticulously evaluated to identify one that delivers precise predictions of student performance, leading to an effective restructure of educational landscapes to replicate the immersive and engaging experience of in-person classes, fostering a conducive environment for students to wholeheartedly focus on academic pursuits. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Students

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Investigation of Augmented Reality in the Agriculture Industry and Its Future 11th

Investigation of Augmented Reality in the Agriculture Indust...

引用

11th International Conference on Intelligent Computing and Applications, ICRTC 2023

作者： Pavithra, K. Geetha, A. Department of Computer Science and Engineering Alliance University Bengaluru India Alliance College of Engineering and Design Alliance University Bengaluru India

ISBN: (纸本)9789819717231

The technology of augmented reality (AR) has the potential to boost overall productivity in the agricultural sector. It can be used with other technologies including robotic machinery, artificial intelligence (AI), the Internet of Things (IoT), predictive analytics, and precision algorithms. Along with the growing food demand, several microeconomic factors are anticipated to encourage the spread of AR in the agricultural sector. New farmers can now receive creative and safer teaching using augmented reality technology. Young farmers can learn about potential mishaps and mitigate future risks as a result of being electronically exposed to agricultural machinery. Given the facts, it is plausible to assume that augmented reality (AR) technologies are still in use. It has been reasonable to assume that AR technology is still being developed and is not yet sufficiently advanced for agricultural applications. However, there is a significant chance that it will be a big success. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Augmented reality

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Facemask Detection System using CNN Model

Facemask Detection System using CNN Model

引用

2023 International Conference on Recent Advances in Science and engineering technology, ICRASET 2023

作者： Senbagavalli, M. Debnath, Saswati Rajagopal, R. Ghildial, Kishkind Alliance University Alliance College of Engineering & Design Computer Science & Engineering and It Department Karnataka Bangalore India Alliance University Alliance College of Engineering & Design Computer Science & Engineering Department Karnataka Bangalore India

ISBN: (纸本)9798350306927

Global trade and transportation have been impacted by the COVID-19 epidemic, which has quickly affected our everyday activities. One of the best ways to stop the Covid-19 virus from transmitting is to wear a face mask. As a result, the World Health Organization (WHO) advised wearing masks as a precaution in crowded areas. Not only COVID-19, wearing mask can reduce the risk of many infectious diseases. In certain places, diseases caused by bacteria, viruses, fungi, or parasites spread quickly due to the inappropriate usage of face masks. Many public services providers demand that their clientele participate in their services while suitably dressed in masks. Therefore, identifying face masks has become a crucial duty in supporting global civilization. This paper develops face mask detection system with an alert that detects the presence of a mask in real-time. The proposed system accurately examines the face from the picture and then determines whether it is covered by a mask or not. A notification can be issued to the administrator if the camera records an unrecognizable face. After that, the administrator will be able to track down the infringer. © 2023 IEEE.

关键词： Viruses

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Attention Based CNN to Improve Identification of Ischaemia and Infection in DFU

Attention Based CNN to Improve Identification of Ischaemia a...

引用

2023 International Conference on New Frontiers in Communication, Automation, Management and Security, ICCAMS 2023

作者： Bhowmick, Priya Revanth, K. Lakshmi, Parlapalli Das, Sujit Kumar Babu, Tina Alliance University Alliance College of Engineering and Design Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350317060

Diabetic foot ulcers (DFUs) frequently occur as complications of diabetes. Identifying infection and ischaemia in DFUs is crucial for wound assessment and treatment planning. Recent advancements in deep learning have shown promise in classifying infection and ischaemia in DFUs using computerized methods. Many modern systems for classifying DFU images rely on deep neural networks, particularly convolutional neural networks. These networks analyze distinctive features and predict class probabilities based on these features. During testing, predictions are made using individual input images and trained parameters. To harness the information in training data more effectively, this study introduces the concept of an attention layer. This layer aids neural networks in memorizing long sequences of data, mitigates issues like exploding gradients, and enhances model accuracy. Experimental results demonstrate that this proposed approach significantly improves the accuracy of DFU infection and ischaemia detection. © 2023 IEEE.

关键词： Deep neural networks

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Bearing Error Diagnosis Using Deep Learning and Convolution Neural Network 2

Bearing Error Diagnosis Using Deep Learning and Convolution ...

引用

2nd International Conference for Innovation in technology, INOCON 2023

作者： Martin, Richard Achary, Rathnakar Shelke, Chetan J Alliance University Alliance College of Engineering and Design Department of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798350320923

Fault diagnostics and prognostics are essential issues. Industrial plants will be under a huge amount of pressure that maintains unpredictable interruption, system failures, and safety issues to a minimum, that necessitates identifying and eliminating potential issues as quickly as possible. Intelligent problem diagnosis is a promising technique because of its capacity to handle gathered signals quickly and effectively while also offering reliable diagnosis findings. Numerous authors have validated deep learning and machine learning approaches for identifying bearings failures, the findings have mostly been confined to tiny train and test datasets, with the input data modified to achieve high accuracy. In this article, original data of accelerometer sensor was loaded into unique periodic sequencing prediction algorithm that develops an edge fault diagnosis technique. We utilize identical frequency patterns as inputs to an innovative deep neural Long-Short-Term-Memory, Recurrent Neural Network to diagnosis bearings insufficiency at excellent accuracy inside the least time (CRNN). Without the use of database adjustment, the technique would acquire the maximum level of competence in the industry. The fault diagnostic method's efficacy and applicability are demonstrated by comparing the findings to those of other intelligent fault detection systems using two widely known benchmark real vibration datasets. © 2023 IEEE.

关键词： Recurrent neural networks

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An Evaluation of Machine Learning Techniques for Detecting Banking Frauds

An Evaluation of Machine Learning Techniques for Detecting B...

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2023 International Conference on Self Sustainable Artificial Intelligence Systems, ICSSAS 2023

作者： Rajagopal, R. Senbagavalli, M. Debnath, Saswati Deepu, K. Darshan, K. Varun Tejas, K.S. Alliance College of Engineering and Design Alliance University Department of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798350300857

Credit cards are an essential part of every person's daily life in the modern world. Customers use their credit cards and online transactions to make purchases for their requirements. To reduce the risk of defaulters, banks and other financial institutions consider customer credit applications. Credit risk is the increase in debt owed by a customer who repeatedly misses payments on their bills. The precision and recall measures received greater attention than the metrics for accuracy. Based on the False Negative value of the confusion metrics, logistic regression is the best model after comparison with the precision-recall curve. Additionally, a GUI (Graphical User Interface) was built after altering the logistic regression's threshold value and it successfully forecasted whether a customer was a defaulter or not. It is a problem to categorize clients' credit payment defaulters and non-defaulters. The basic objective of risk prediction is to evaluate each customer's credit risk and reduce it using information from financial statements, customer transactions, and repayment history. The damage and unpredictable nature. Different methods, including logistic regression and decision trees, are used to create risk prediction models. © 2023 IEEE.

关键词： Decision trees

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