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Design and Development of Expense App

Design and Development of Expense App

引用

2024 International Conference on Advances in Computing Research on science engineering and Technology, ACROSET 2024

作者： Girdhar, Gunit Kumar, Sachin Bhardwaj, Anuj Sharma, Megha Chandigarh University Dept. of Computer Science & Engineering Mohali India

ISBN: (纸本)9798350388800

With the help of Expense Tracker, you can easily and intuitively manage your revenue and costs while keeping track of your everyday, weekly, monthly, and annual spending. Users may choose the kind of expenditure, add location, upload more data, choose the amount of expense, and more. Additionally, the relational database will save this data. Users have the option to see and sort spending weekly, monthly, or annually. We may track the spending and minimize the amount of human computations for their costs by employing this. This allows a user to enter their income to get an estimate of their daily costs, and the results are saved for each individual user. Individuals who frequently go on excursions or to the theatre with friends might use this tracker to keep tabs on their spending. They will find it simple to split the bill using this tracker. This will show graphs according to the chosen perspective. Additionally, a user may write in this TR his monthly income or monthly expenditure cap. An integrated feature set is offered by this tracker system to assist you in managing your spending and cash flow. © 2024 IEEE.

关键词： Cost benefit analysis

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A Rubric-Centric Approach for Automated Test Correction Utilizing RAG and Fine Tuning 4

A Rubric-Centric Approach for Automated Test Correction Util...

引用

4th International Conference on Technological Advancements in Computational sciences, ICTACS 2024

作者： Harshavardhan, G. Singh, Kulvinder Chandigarh University Dept. of Computer Science & Engineering Punjab India

ISBN: (纸本)9798350387490

Assessments are a popular method to quantify a person's knowledge, skill, or cognitive ability. However, to grade such assessments, particularly subjective questions requires a significant amount of time and effort for the examiners. There is also a degree of variance due to inconsistency from different examiners. Thus institutions often resort to taking objective-type questions, which may not completely capture a person's understanding. Unfortunately, many other proposed solutions, tend to be difficult to implement and scale. To address these issues, this paper proposes an innovative method to automate the grading of subjective assessments by leveraging recent advances in artificial intelligence. This approach aims to replicate the grading style of human experts, offering significant benefits for educational institutions, faculty, and students by enhancing the efficiency and quality of grading. Widespread adoption can help increase the quality of education overall, resulting in more qualified graduates. This method involves using a rubric to fine- tune pre-trained language models, providing them with clear guidelines to ensure consistent and accurate grading. Inspired by how rubrics guide human examiners to minimize variation, this approach will use rubrics to instruct the model on the most relevant grading criteria. Utilizing such clearly defined and precise grading criteria, users can obtain reliable results, comparable to human experts. © 2024 IEEE.

关键词： Natural language processing systems

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BeTS: Abstractive Text Summarization with Transfer Learning 2

BeTS: Abstractive Text Summarization with Transfer Learning

引用

2nd IEEE International Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation, IATMSI 2024

作者： Bhardwaj, Meher Ethari, Hrishikesh Charan, Annepu Sai Singh Moirangthem, Dennis Iiit Manipur Dept. of Computer Science and Engineering Imphal India

ISBN: (纸本)9798350360523

We introduce BeTS, which stands for Bert Transformer Summarizer, an abstractive summarization model that is built using transfer learning. Our model consists of a pre-trained BERT encoder and a generative transformer decoder to produce abstract summaries. The model is trained by a simple two step process, where the decoder is first trained and then the entire model is fine-tuned end-to-end. We train the model with CNN/Daily Mail summarization dataset and use the trained weights for transfer learning on the AMI dialog summarization dataset. We successfully demonstrate that our model outperforms the baseline models by at least 7.65 ROUGE points on the AMI data. We also demonstrate that our model shows good generalization performance by testing on different unseen input data and compare the generated results. We are able to generate significantly better summaries on inputs such as dialog compared to the current state-of-the-art model. © 2024 IEEE.

关键词： Decoding

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Performance Comparison of Machine Learning Algorithms for Parkinson's Disease Detection 14

Performance Comparison of Machine Learning Algorithms for Pa...

引用

14th International Conference on Cloud Computing, Data science and engineering, Confluence 2024

作者： Gupta, Ipsha Vashisth, Dhwani Garg, Rakesh Amity University Dept. of Computer Science and Engineering UP India

ISBN: (纸本)9798350344837

Parkinson's Disease affects the neural system and can be detected using biomarkers, physiological and motor disruptions exhibited by patients. Several advancements and research on Machine Learning approaches have been carried out and tested to diagnose PD in the early stages of its inception by continuous monitoring of physical actions of patients. In this study we: (a) conduct a comprehensive analysis of existing machine learning algorithms to detect PD patients, (b) summarize and categorize the findings of efficiency of these algorithms, (c) test these algorithms on speech recordings of patients, (d) provide the model with best accuracy with an overview of all the models previously researched or currently in use. The benefits and shortcomings of the referenced models are thoroughly contrasted and explored for better accessibility for future research. © 2024 IEEE.

关键词： Support vector machines

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Machine Learning - Driven Approaches for Detection and Analysis of Phishing attacks: A Robust Framework for Cybersecurity 7

Machine Learning - Driven Approaches for Detection and Analy...

引用

7th International Conference on Inventive Computation Technologies, ICICT 2024

作者： Sarvan, N.D.S.S. Sai Ram Reddy, Venkata Jain, Darshan Rakesh, Guntupalli Anguraj, Dinesh Kumar Kl University Dept. of Computer Science Engineering Guntur India

ISBN: (纸本)9798350359299

Phishing is the attempt to trick someone into disclosing confidential and private information by using internet communication, such as bank account details, passwords, credit/debit card details, and usernames. The accuracy of foreseeing such phishing assaults can be substantially enhanced through utilizing these algorithms. The primary objective of this research work is to enhance internet security by providing a dependable and productive method for detecting phishing websites. To achieve this, a machine learning-based approach was employed, utilizing various models trained on a dataset of phishing website attributes. The study evaluated a range of machine learning algorithms and classifiers, including Naive Bayes, Support Vector Machine, K-Nearest Neighbors, Gradient Boosting, Decision Tree, Random Forest, Bagging, Voting, and AdaBoost Classifiers. These evaluation parameters are used to determine the efficiency of the machine learning models including F1 Score, Accuracy, Precision, and Recall values. By training the dataset with key variables from credible websites, the Bagging Classifier algorithm demonstrated a substantial improvement, achieving an accuracy rate of 98.4% resulting in a significant enhancement in phishing website detection compared to other methods. © 2024 IEEE.

关键词： Support vector machines

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Optimizing Kidney Stone Detection with Vision Transformers and CNNs: A Transfer Learning Strategy

Optimizing Kidney Stone Detection with Vision Transformers a...

引用

2025 International Conference on Pervasive Computational Technologies, ICPCT 2025

作者： Kulkarni, Supritha Suhaas Rao, S. Chopra, Tushar Kumar Yeshwanth, S.P. Bhanushree, K.J. Suneetha, K.R. Bangalore Institute of Technology Dept. of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798331508685

Kidney stones are a common and impactful urological condition, with timely and accurate diagnosis being critical to prevent complications such as renal damage and avoid invasive treatments. This study explores the use of advanced deep learning models, including Vision Transformers (ViT), Swin Transformer and Convolutional Neural Networks (CNNs) - specifically XResNet-50, VGG-16, MobileNetV2 and AlexNet - to enhance kidney stone detection from CT scan images. Leveraging a robust dataset of annotated CT scans in JPG format, the approach employs transfer learning which leverages features learnt from the ImageNet dataset to optimize model performance, focusing on classifying cases as either "Kidney Stone Present"or "Kidney Stone Absent". To further improve diagnostic reliability, the Grad-CAM (Gradient-weighted Class Activation Mapping) identifies and localizes kidney stones within CT images, enhancing understanding of the model's decision-making and supporting interpretability. Addressing challenges related to dataset imbalance, model interpretability, and generalizability across healthcare settings. By refining these models for kidney stone detection, the main objective of this work is to build a reliable diagnostic tool that aids in clinical judgement and treatment planning, ultimately enhancing patient outcomes and encouraging broader clinical adoption. © 2025 IEEE.

关键词： Convolutional neural networks

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A Review on Cosmetic Product Recommendation Using Deep Learning 4

A Review on Cosmetic Product Recommendation Using Deep Learn...

引用

4th International Conference on Soft Computing for Security Applications, ICSCSA 2024

作者： Chouhan, Ruchika Barde, Snehlata Sage University Dept. of Computer Science Engineering Indore India

ISBN: (纸本)9798331515720

Cosmetic products play an important role in every one's life. Cosmetic products also called as makeup are substance that is applied on the body especially on face. The modern lifestyle can be demanding, leaving little time for self-care. However, it's crucial to prioritize self-care, including skincare, despite hectic schedules. Indeed, relying solely on best-seller products or in-store recommendations may not always be effective in determining compatibility between a skincare product and an individual's skin condition. This is because everyone's skin tone and type of the skin is unique, with varying sensitivities, concerns, and needs. Many people buy their goods online today, using various e-commerce sites. So, to choose the best skincare product during online purchasing is difficult, since it depends entirely on skin type and usage. The main aim of this paper is to survey and explores the existing recommendation systems designed to assist users in selecting cosmetics personalized to their unique skin type, by using advanced algorithms and machine learning. This study has reviewed various methods like face recognition, cosmetic product recognition, tag recommendation, facial skin image classification using convolution neural network, a new examples-rules guided deep neural network approach. Through a comprehensive review of literature and existing research, this paper examines the methodologies, algorithms, and techniques employed in cosmetic recommendation systems. Key factors such as user preferences, skin type, skin concerns, and product attributes are analysed to understand how recommendation systems use this information to generate personalized recommendations. © 2024 IEEE.

关键词： Cosmetics

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Harnessing Transformative Potential of UAV-Based AI-Powered Data Analysis for Emission Monitoring 6

Harnessing Transformative Potential of UAV-Based AI-Powered ...

引用

6th International Conference on Computational Intelligence and Networks, CINE 2024

作者： Namdev, Mayank Dholaria, Jay Rathi, Meet Manipal University Dept. of Computer Science Engineering Jaipur India

ISBN: (纸本)9798331516796

With the growing pressure to reduce environmental pollution, the combination of Unmanned Aerial Vehicles (UAVs) and Artificial Intelligence (AI) offers a viable path forward for modernizing emission monitoring. This research investigates the revolutionary potential of UAV-based AI-powered data analysis for emissions monitoring, with a particular emphasis on the comparison of XGBoost and CatBoost algorithms. Real-time monitoring, identification, and analysis of pollutants can now be done with remarkable accuracy and efficiency thanks to UAVs outfitted with modern sensors and AI algorithms. This novel methodology has significant advantages over typical monitoring approaches, including increased spatial coverage, lower costs, and higher data quality. Furthermore, AI integration provides predictive analytics and anomaly identification, allowing for proactive environmental risk mitigation interventions. Despite its enormous promise, challenges such as regulatory frameworks, privacy concerns, and technological restrictions must be solved before widespread usage. Overall, the combination of UAVs and AI, as well as the comparison of XGBoost and CatBoost, hold enormous promise for revolutionizing emission monitoring by providing a scalable and versatile solution to protect environmental quality and human health. © 2024 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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A Study on Deep Fake Face Detection Techniques 3

A Study on Deep Fake Face Detection Techniques

引用

3rd International Conference on Applied Artificial Intelligence and Computing, ICAAIC 2024

作者： Arya, Mudit Priyanshu Upwan Akash Goyal, Umang Chawla, Simran Chandigarh University Dept. of Computer Science and Engineering Punjab India

ISBN: (纸本)9798350375190

Advanced technology and the widespread use of deep fake technology has rendered the digital landscape vulnerable to deceptive manipulations, particularly through the creation of synthetic face images. This comprehensive review precisely traces the historical trajectory of deep fake faces, encompassing their evolution and its impact on various domains. It also investigates into the growing concerns regarding misinformation and privacy breaches. The literature review analyzes important works, pivotal milestones, and notable case studies, providing a complete understanding of the dynamic landscape of deep fake faces. This study analyzes existing detection techniques, ranging from neural networks to machine learning approaches, offering insights into the complexities of current methodologies. It sheds light on both the strengths and limitations of these techniques, emphasizing the need for robust solutions to counter adversarial attacks and address data scarcity. Building on this analysis, this study explores breakthroughs within the field of deep fake detection, highlighting instances where identification has been successful. It also underscores the ethical considerations that come with the evolution of these technologies. In proposing innovative solutions and discussing potential optimizations for existing techniques, the review explores interdisciplinary approaches and emerging technologies like blockchain and explainable AI. The findings provide a summary of key insights collected from the literature, identify research gaps, and project the future of deep fake face detection. This study concludes by emphasizing the ongoing societal impact of these developments in the digital era. © 2024 IEEE.

关键词： Face recognition

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Cybersecurity Threat Detection Using Deep Learning and Anomaly Detection Techniques

Cybersecurity Threat Detection Using Deep Learning and Anoma...

引用

2024 International Conference on Knowledge engineering and Communication Systems, ICKECS 2024

作者： Rajendran, T. Mohamed Imtiaz, N. Jagadeesh, K. Sampathkumar, Balaji Rajalakshmi Institute of Technology Department of Computer Science and Engineering Chennai India Nagarjuna College of Engineering and Technology Dept. of Information Science and Engineering Bangalore India Sri Venkateswaraa College of Technology Dept. of Computer Science and Engineering Chennai India Akshaya College of Engineering and Technology Dept. of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350359688

Through the use of combined deep learning and anomaly detection approaches, this research investigates the area of cybersecurity threat detection. The study proves the framework's extraordinary success in recognizing as well as mitigating diverse cyber dangers, including insider threats and zero-day attacks, through practical testing and case studies. The framework establishes itself as a proactive cybersecurity solution adaptive to the changing threat landscape due to its high accuracy of 95%, low amount of false positives 5%, and quick reaction times. The recommendations cover user training, scalability, regulatory compliance, including cross-disciplinary collaboration. Behavioral analysis, AI explainability, IoT security, and countering quantum computing concerns should be the main areas of future research. These revelations help the cybersecurity industry become more resilient and well-prepared. In this research investigation, experiments on threat detection were conducted using deep learning algorithms, with a specific focus on Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs). The findings of the study reveal that a detection accuracy of 93% was achieved by CNN, while a higher accuracy of 96% in threat detection was exhibited by RNN. © 2024 IEEE.

关键词： Zero-day attack

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