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Machine Learning optimization techniques for Accurate Melanoma Diagnosis

Machine Learning Optimization Techniques for Accurate Melano...

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Automation and Computation (AUTOCOM), International conference on

作者： Jaspreet Sidhu Jason Marandi Ashu Katyal Nagaraj Patil Kalyan Acharjya Veera Nagaiah Maddikayala Centre of Research Impact and Outcome Chitkara University Rajpura Punjab India Department of Computer Science and Engineering Siksha ‘O’ Anusandhan (Deemed to be University) Bhubaneswar Odisha India Chitkara Centre for Research and Development Chitkara University Baddi Himachal Pradesh India School of Engineering and Technology Mechanical Engineering JAIN (Deemed to be University) Bangalore Karnataka Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Department of Computer Applications (DCA) Presidency College Bengaluru India

ISBN: (数字)9798331542375

ISBN: (纸本)9798331542382

Melanoma is the deadliest form of skin cancer, claiming more than 7,000 lives each year in the United States; roughly 67, where there were almost over196 new cases URITY CASES ANNUALLY IN THE US. Because of its visual similarity to non-melanomas, it is more important than ever that melanoma be discovered early for effective treatment. As per the research, machine learning (ML) techniques have gained high popularity for automated image analysis and classification during melanoma diagnosis, and they have assisted dermatologists in diagnosing efficiently. At the same time, the performance of ML models relies on how optimization techniques are implemented in training. ML optimization tools buck as gradient descent and poor initialization may result in black-box solutions that could lead to misdiagnosis. New approaches with deep learning and reinforcement have led to dramatic improvements in the performance of machine-learning models over the last several years. Historically, deep learning algorithms have proven highly accurate in classifying melanoma images because they can automatically learn complex features from the data. In addition, they tried to optimize the feature selected and make your ML model interpretable by using the reinforcement learning method, which searches for an optimal solution through a trial-and-error process. Conclusion optimization of machine learning techniques is essential for providing accurate melanoma diagnosis. Recent years of the digital revolution, stupefied by advanced arguments like Deep Learning and Reinforcement learning models, have contributed to high accuracy in Machine learning models a dream come true for dermatologists with timely detection & cement of this deadly disease.

关键词： Deep learning visualization Accuracy Refining Melanoma Machine learning Reinforcement learning Feature extraction optimization Tuning

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Research on Enhancing Classification and Coreset Selection with KNN Based Framework

Research on Enhancing Classification and Coreset Selection w...

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International conference on Artificial Intelligence in Information and Communication (ICAIIC)

作者： Jun Yeong Lee Dong Seog Han Sin Jae Kang School of Electronic and Electrical Engineering Kyungpook National University Daegu Republic of Korea

ISBN: (数字)9798331507022

ISBN: (纸本)9798331507039

In the field of machine learning, effective learning strategies play a critical role in enhancing model performance and efficiency. This paper utilizes Shapley entropy values to increase inter-class distances to enhance classification tasks. The proposed framework integrates these values with adversarial techniques and extra-intra object distance minimization strategies inspired by adversarial optimization and object distance refinement techniques. Shapley entropy provides a nuanced way of addressing the limitations of recent OoD(out of distribution) loss functions, which primarily rely on adversarial methods but struggle with complex inter-cluster relationships. By leveraging a knn(k-nearest neighbor) based approach, Shapley entropy effectively defines multiple inter-cluster relationships, improving class separation and intra-class cohesion in a more robust and scalable manner. Moreover, the shapley entropy demonstrates superior efficiency by simultaneously addressing coreset selection and enhancing classification performance, making it applicable across both tasks seamlessly. Experimental results include class-wise accuracy, t-SNE visualizations validated on CIFAR-10, and the application of active learning.

关键词： Deep learning visualization Accuracy Active learning Organizations Nearest neighbor methods Minimization Entropy optimization

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Enhanced Diabetes Prediction Through Systematic Analysis of Machine Learning techniques and Data optimization

Enhanced Diabetes Prediction Through Systematic Analysis of ...

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Visual Analytics and Data visualization (ICVADV), International conference on

作者： Saroj Kumar Behera Kedarnath Panda M Vamsi Krishna P Ram Kishore Kumar Reddy P Lakshmi Supriya Kiran Kumar Reddy Penubaka Department of CSA Gandhi Institute of Engineering and Technology University Odisha India Department of CSE Gandhi Institute of Engineering and Technology University Odisha India Department of Computational Intelligence (CSE - AI & ML) Malla Reddy College of Engineering and Technology Hyderabad India Department of EEE Mahatma Gandhi Institute of Technology Hyderabad India Department of EEE Mahatma Gandhi Institute of Technology (A) Gandipet Hyderabad India Department of CSE-AIML MLR Institute of Technology Hyderabad India

ISBN: (数字)9798331521394

ISBN: (纸本)9798331521400

This research concentrates on applying various machine learning algorithms to develop an effective and efficient predictive model for screening for diabetes prediction. Its primary aim is to enhance the performance of the model and time to learn and generalize by performing feature engineering, feature selection, and hyperparameter tuning all apt. The strategy begins with preprocessing data, where problems of missing or incorrect values are addressed and duplicate records are eliminated on a dataset from Kaggle. SMOTE is employed in the case of class imbalance; new samples for the minority class are created, while outliers in datasets are identified using the DBSCAN algorithm. The required features are achieved through the Wolf Optimized Recursive Elimination (WORE), the union of the Grey Wolf optimization (GWO) algorithm, and Recursive Feature Elimination (RFE). The model is constructed through the Random Forest-Enhanced Logistic Regression (RFELR) model, a hybrid of both Random Forest and Logistic Regression in which Random Forest is employed to obtain a nonlinear relationship, combined with logistic regression for a linear decision boundary. The final adjustment of the models is carried out using Grid Search as the Hyperparameter tuning method. These indicate that the overall performance reaches 91.5 % accuracy while precision, recall, and Fl were at 89.6%, 83.1 %, and 87.5% respectively. The paper has pointed out the appropriateness of the suggested approach in predictive performance improvement for diabetes detection to a significant degree beneficial to clinical use.

关键词： Logistic regression Machine learning algorithms Systematics Visual analytics Forestry Predictive models Diabetes optimization Tuning Random forests

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Design of a Lightweight Privacy Communication Network System for Medium Enterprises

Design of a Lightweight Privacy Communication Network System...

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IEEE Information Technology and Mechatronics Engineering conference (ITOEC)

作者： Ruixiang Suo Wenxuan Qiao Xinyang Bai Yuyang Zhang Ping Dong School of Electronic and Information Engineering Beijing Jiaotong University Beijing China

ISBN: (数字)9798331529482

ISBN: (纸本)9798331529499

Medium and large enterprises need to exchange confidential information frequently. Traditional encryption techniques can only protect the security of communication data, but the privacy of the communicating entities' identity information cannot be effectively protected. Currently, common solutions include virtual private networks (VPNs) and single/multiple agent-based forwarding mechanisms. However, the egress nodes of VPNs are susceptible to attacks and monitoring; agents in single-agent forwarding mechanisms are easy targets for single points of failure and attacks. Medium and large enterprises urgently need a solution that ensures communication privacy while being cost-effective and easy to manage. Therefore, this paper proposes a lightweight privacy communication network system based on a multi-agent mechanism to realize the non-association of communication parties and provide transparent transmission services. In order to schedule and manage the whole system flexibly, this paper implements a remote service and a visualization service in a Directory Server. In addition, an intelligent multi-objective optimization analysis of the route selection algorithm is introduced to ensure the anti-tracking and high efficiency of data transmission; meanwhile, traffic obfuscation technology is used to prevent attackers from recognizing the communication content and the identities of both parties through the traffic pattern, which improves the transmission and management security of the system. Test results show that the system effectively protects information communication relationships in medium and large enterprises.

关键词： Privacy Data privacy Technological innovation Data visualization Traffic control Virtual private networks Internet Communication networks Protection optimization

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Innovative VLSI Design for High- Energy Efficiency Approximate Reverse Carry Save Adders

Innovative VLSI Design for High- Energy Efficiency Approxima...

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Visual Analytics and Data visualization (ICVADV), International conference on

作者： M K Aravind Kumar M Karthick S Kishore R Poovarasan Department of ECE KS Rangasamy College of Technology Tiruchengode Tamil Nadu India

ISBN: (数字)9798331521394

ISBN: (纸本)9798331521400

The increasing demand for energy-efficient and high-performance computing in modern electronic systems has driven the development of approximate computing techniques. The methodology presents a Innovative VLSI implementation of an Approximate Reverse Carry Save Adder (ARCSA) optimized for applications that accept trade-offs between accuracy, speed, and power consumption. The ARCSA architecture utilizes approximation principles to reduce computational complexity, achieving significant improvements in delay, power dissipation, and hardware area compared to traditional adders The design is implemented using Verilog HDL, with functionality verified through simulation in ModelSim. This ensures that the proposed ARCSA maintains functional correctness while delivering performance improvements. The synthesized design is analyzed further using Xilinx Vivado for FPGA implementation, where performance metrics such as delay, power dissipation, and area utilization are thoroughly evaluated. The results demonstrate that the ARCSA achieves a balanced trade-off between power efficiency and computational performance, offering a compelling solution for low-power, high-speed applications in mobile and embedded systems. This research highlights the potential of approximate computing in achieving significant efficiency gains without compromising system functionality. By leveraging approximation in critical components such as adders, it is possible to design systems that are not only faster and more power-efficient but also capable of handling error-tolerant tasks with greater resource efficiency. The proposed ARCSA architecture stands as a promising candidate for future systems where energy efficiency and performance optimization are paramount.

关键词： Approximate computing Computer architecture Very large scale integration Energy efficiency Hardware Delays Computational efficiency Hardware design languages Adders optimization

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Optimizing Crop Traits with ANN and Genetic Innovations for Sustainable Agriculture

Optimizing Crop Traits with ANN and Genetic Innovations for ...

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Visual Analytics and Data visualization (ICVADV), International conference on

作者： Radha Mahendran Ramanakar Reddy Danda Asma Anjum Rashmi Abhay Kamde Bhagyashree Shailesh Firake Jayshree Suhas Wasnik Department of Bioinformatics Vels Institute of Science Technology and Advanced Studies Chennai India Architect Hyderabad Telangana India Department of Computer Science and Engineering HKBK College of Engineering Visvesvaraya Technological University Bangalore India Department of Electrical Engineering K J Somaiya Polytechnic Mumbai Maharashtra India

ISBN: (数字)9798331521394

ISBN: (纸本)9798331521400

Sustainable agriculture requires new strategies to tackle difficulties such as rising food demand, environmental pressures, and resource constraints. This study introduces a sophisticated approach that combines artificial neural networks (ANNs) with genetic advancements to enhance crop characteristics. The system attains enhanced accuracy (98.1%), F1 score (0.97), and reduced RMSE (0.012) by utilising high-dimensional genetic, phenotypic, and environmental data, surpassing baseline models. The technique integrates ANN predictions with gene-editing frameworks, guaranteeing accurate interventions and effective trait optimisation. The results demonstrate considerable improvements in forecast accuracy and resource efficiency compared to current techniques, creating a scalable, data-driven framework to promote sustainable agriculture practices worldwide.

关键词： Precision agriculture Technological innovation Adaptation models Accuracy Visual analytics Crops Artificial neural networks Genetics Sustainable development optimization

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An Efficient Algorithm for Generating Interactive visualization of Large Datasets

An Efficient Algorithm for Generating Interactive Visualizat...

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Advances in Computer Science, Electrical, Electronics, and Communication Technologies (CE2CT), International conference on

作者： Chandani Sharma Venkata Suresh Babu Chilluri Ankit Kumar Shweta Goyal CSE Department MMICTBM (MCA) Maharishi Markandeshwar (Deemed to be University) Mullana-Ambala Haryana Intuit Inc. Intuit Inc. Mountain View CA USA Department of Computer Science and Engineering Chandigarh University Mohali India Department of Electrical Engineering Graphic Era Deemed to be University Dehradun India

ISBN: (数字)9798331518578

ISBN: (纸本)9798331518585

Easy-to-use visualisation tools are essential for identifying patterns in unstructured data as it grows larger and more complicated. The majority of large data visualisation techniques either knowledge of a programming language or access to commercial tools. These days, data is growing in size, and statistical visualisation of the data needs numerous displays to represent all of the data's viewpoints. Model tuning, optimisation, and quick decision-making are greatly aided by the intriguing patterns produced by the models that are visualised. However, traditional techniques like histograms, pie charts, box plots, and bar graphs are typically insufficient to adequately communicate the intriguing pattern that needs to be extracted from massive datasets. As a result, this paper introduces a tree-plot method that uses the h2o package's in-memory node technique to store the enormous dataset in memory. Data. Tree's charting capabilities were employed to display the modelled trees, which were built using an Ada Boosted Model as the underlying learning methods. Visualising the modelled tree and evaluating the effectiveness of the data training process are based on the AUC, RMSE, execution procedure time, and MSE results.

关键词： Water Training Learning systems Technological innovation Histograms Tree graphs Data visualization optimization Tuning Regression tree analysis

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Application Research on Quality Evaluation of College Graduation Thesis Based on Artificial Intelligence

Application Research on Quality Evaluation of College Gradua...

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International conference on Educational and Information Technology (ICEIT)

作者： Xiaoli Yang Xin Xie Rongyu Cui College of Higher Vocational and Technical dept Chengdu Neusoft University Chengdu China

ISBN: (数字)9798331540883

ISBN: (纸本)9798331540890

This article focuses on the optimization of the quality evaluation system for college graduation theses, and proposes a solution based on artificial intelligence technology to address the limitations of traditional manual evaluation methods, such as subjectivity, low efficiency, and inconsistent evaluation standards. A quality evaluation model for graduation thesis was developed, which integrates features of layout, writing style, and text semantics. This model utilizes natural language processing and machine learning techniques to automate the processing and analysis of graduation thesis data, aiming to improve the objectivity, accuracy, and efficiency of evaluation. Specifically, the layout features focus on the visual presentation of the paper, while the writing style features emphasize language expression and argumentation, and the deep semantic features delve into the academic value and innovative points of the paper. By integrating these three characteristics, the model can comprehensively and deeply reflect the quality level of the graduation thesis. To verify the effectiveness and reliability of the model, this study borrowed Tencent's CoQAN model idea for experimental verification. The experimental results show that the model has significant accuracy and stability in the quality evaluation of graduation theses, providing strong technical support for the optimization of the quality evaluation system of university graduation theses. In addition, the application prospects and challenges of artificial intelligence technology in the quality evaluation of graduation theses were discussed, and further research directions and suggestions were proposed.

关键词： Training visualization Technological innovation Accuracy Layout Semantics Writing Artificial intelligence optimization Standards

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Sequential optimization of Healthcare Processes using Reinforcement Learning and Sequential Function Models

Sequential Optimization of Healthcare Processes using Reinfo...

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Automation and Computation (AUTOCOM), International conference on

作者： Malatesh Akkur B.P. Singh Pachayappan R Takveer Singh Binayak Panda Pradeep Marwaha School of Sciences Physics & Electronics JAIN (Deemed to be University) Bangalore Karnataka India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Department of Computer Applications (DCA) Presidency College Bengaluru India Centre of Research Impact and Outcome Chitkara University Rajpura Punjab India Department of Computer Science and Engineering Siksha ‘O’ Anusandhan (Deemed to be University) Bhubaneswar Odisha India Chitkara Centre for Research and Development Chitkara University Baddi Himachal Pradesh India

ISBN: (数字)9798331542375

ISBN: (纸本)9798331542382

The optimization of healthcare processes is so important, and it directly affects outcomes. However, traditional methods like Six Sigma and Lean cannot manage the complexities of complex and dynamic healthcare processes. Thus, alternative solutions must strive to enable the optimization of healthcare services. We intend to use reinforcement learning (RL) and sequential function model (SFM) models combined as a new Sequential optimization framework for healthcare process optimizations in this work. It is a machine learning tool that provides equivalent human actions by deciding to choose certain future moves independently. Results for the second experiment: SFM visually explains process flow and capiases dependency between different steps in a sort order. These two techniques are combined in the proposed framework to learn and optimize healthcare processes sequentially. Sequential optimization: the RL algorithms started learning from previous experiences to optimize the best decisions at different process steps. From there, the SFM provides a visual rendering of this streamlined process. The effect of the process changes can be experimented with via simulation and altered as such. This iterative process improves efficiency in processes and reduces costs to healthcare systems. The proposed framework may transform healthcare processes' optimization and, by extension, improve patient outcomes.

关键词： visualization Sequential analysis Medical services Reinforcement learning Transforms Rendering (computer graphics) Resource management Iterative methods optimization Six sigma

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Stock Price Prediction using LSTM with Toeplitz Matrix-Driven Feature Extraction for Enhanced Forecasting Accuracy

Stock Price Prediction using LSTM with Toeplitz Matrix-Drive...

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Visual Analytics and Data visualization (ICVADV), International conference on

作者： Kanchana R F. Mary Harin Fernandez Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai India

ISBN: (数字)9798331521394

ISBN: (纸本)9798331521400

The research methodology studies the use of Long Short-Term Memory networks in predicting stock prices, with the case of Tata Motors' historical price data reporting. A unique aspect of this study is the introduction of a Toeplitz matrix to capture the sequential information present in the stock data, thus, the model can better identify time-dependent correlations. Data mining includes cleaning, normalizing, and restructuring the dataset for the best application of the LSTM model. The study divides the data into training and testing sets for the model to learn from the past and then make the price predictions. Overall performance evaluation exhibits how the model was able to follow intricate price movements and provide a concrete reality prediction of the future prices. In addition to that, results verifying the efficiency of LSTM networks in processing the non-linear and dynamic relationships of price stocks in the respective financial markets, which in turn provides confidence to explore further developments and improvements in prediction. This is demonstrating the capability of machine learning algorithms to be the most useful in economic analysis when novel and creative techniques are implemented that help to boost the precision of model forecasting even in uncertain market conditions.

关键词： Machine learning algorithms Correlation Time series analysis Predictive models Motors Data models Data mining Forecasting Long short term memory optimization

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