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Blockchain Integration for Robust Intellectual Property Protection in New Product Development (NPD) 3

Blockchain Integration for Robust Intellectual Property Prot...

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3rd International Conference on Electronics and Renewable Systems, ICEARS 2025

作者： Chinnasamy, P. Subashini, B. Nijanthan, Nijanthan Madasamy Raja, G. Swetha, Devulapally Sreenivasulu Reddy, L. School of Computing Kalasalingam Academy of Research and Education Department of Computer Science and Engineering Tamilnadu India School of Computing Srmist Department of Data Science and Business Systems Kattankulathur India Paavai Engineering College Tamilnadu Namakkal India Paavai Engineering College Tamilnadu Namakkal India Vaagdevi Engineering College Dept. of Computer Science and Engineering Warangal Dist. Telangana Bolli Kunta506005 India School of Advanced Sciences Kalasalingam Academy of Research and Education Department of Mathematics Tamilnadu Krishnankoil India

ISBN: (纸本)9798331509675

The rapid advancement of Blockchain technology has led to its widespread application in several domains of digital activities, such as e-government concerns and monetary security. This article presents a smart contract-based Blockchain solution for protecting intellectual property rights in new product development. The proposed solution effectively tackles the challenges associated with the confidentiality of personally identifiable information, efficiency in storing transaction-related information, as well as cooperation and monitoring in IPR. Initially, we create a mathematical model based on smart contract enabled Blockchain and intelligent contracts. Subsequently, we develop algorithms for Blockchain generation, and intelligent contracts. The system is suitable for protecting IPR in a blockchain network, by implementation of distributed identity authentication and real-time extensible storage. These features enhance the security and expandability of circuit copyright protection based on blockchain technology. The experimental results demonstrate that the suggested algorithm has decreased the cost for communication and enhanced the effectiveness of storing information and administration. Furthermore, it demonstrates resilience against many common attacks such as double-spending assaults, while maintaining low cost and overhead. Additionally, it exhibits a superior level of security in comparison to three other competing algorithms. © 2025 IEEE.

关键词： Smart contract

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Enhancing Early Detection of Pancreatic Cancer: A Machine Learning Approach with Explainable AI Insights

Enhancing Early Detection of Pancreatic Cancer: A Machine Le...

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Computer Vision and Machine Intelligence (CVMI), International Conference on

作者： Neeraj V Pattanashetti R Mayur Adarsh Prakash Tryambaka B Adiga Ashwini Kodipalli Trupthi Rao Dept. of Computer Science & Engineering Global Academy of Technology Bangalore Karnataka India Dept. of Information science & Engineering Global Academy of Technology Bangalore Karnataka India Dept. of Artificial Intelligence & Data Science Global Academy of Technology Bangalore Karnataka India

ISBN: (数字)9798350376876

ISBN: (纸本)9798350376883

Among the most deadly and complex illnesses in the world, pancreatic cancer is typified by its early asymptomatic stage and subsequent discovery of the disease when it has progressed to an advanced stage. Getting the patients in a correct treatment path at the earliest point is important for suitable intervention and patient outcomes in the long run. We utilized advanced algorithms and model prediction techniques includes, feature extraction, hyperparameter tuning and explainability methods which aims to enhance both the comprehension and predictive accuracy of models employed. The goal is to assess patient health status and diagnose pancreatic diseases (cancer vs. non-cancer) by developing a learning algorithm. We obtained a dataset containing demographic information and urine biomarkers to start the investigation. First stage of our analysis employs a single classifier and later we apply bagging and boosting algorithms to achieve ensemble models. Hyperparameter fine-tuning has got a positive outcome, which has Gradient Boosting and Cat Boosting as its top performing models. The ensemble model produced excellent results $(92.02 \%)$, demonstrating the usefulness of the suggested approach. LIME and SHAP techniques play the key role in model outcome interpretability providing an insight of algorithmic decision-making process. Such a wide range of combinations of biomarkers have a potential of pancreatic cancer detection in early stages and thus will help shape diagnostics strategies in near future.

关键词： Machine learning algorithms Shape Biological system modeling Pancreatic cancer Predictive models Biomarkers Boosting Prediction algorithms Classification algorithms Tuning

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ML-driven Emotion Identification For Feedback Analysis In E-learning Platforms

ML-driven Emotion Identification For Feedback Analysis In E-...

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Emerging Trends in Industry 4.0 (ETI 4.0)

作者： Chengamma Chitteti Runvika Kopparam B.V.S.S. Ganesh Sukerthi Sutraya V Kamakshi Shiridinath Jangam Dept. of Data Science School of Computing Mohan babu university (Erstwhile Sree Vidyanikethan Engineering College) Tirupati India Dept. of CSSE Mohan babu university (Erstwhile Sree Vidyanikethan Engineering College) Tirupati India Dept. of Data Science G. Narayanamma Institute of Technology and Science for women Hyderabad India

ISBN: (数字)9798350373783

ISBN: (纸本)9798350373790

With an emphasis on the cognitive and emotional effects of e-learning platforms, this study fills a research gap concerning user experience studies. The study suggests a nonintrusive technique to evaluate the psychological impacts of e-learning platforms by considering feedback data of users in the form of videos and analyzes emotions from video and decides the user feedback level. This process automates the understanding of feedback in a short amount of time for anyone, without requiring specific knowledge of the system. Feedback analysis for E-Learning platforms using deep learning methods for facial expression analysis is proposed in this work. Convolutional Neural Networks (CNNs), a category of deep learning, has become increasingly popular for its ability to precisely analyze and comprehend emotions from facial photos. These models may efficiently learn and extract essential features by being trained on large datasets of tagged facial expression photos. This application takes video feedback as input for various E-learning platforms and analyzes emotions and displays the predicted emotions on video. A publicly accessible dataset is used to test the proposed technique, and the results show that it achieves ${9 0. 3 \%}$ accuracy. This indicates that the approach is highly effective in accurately recognizing facial emotions.

关键词： Deep learning Training Technological innovation Electronic learning Accuracy Face recognition Feature extraction

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Comprehensive Methodologies for Breast Cancer Classification: Leveraging XAI LIME, SHAP Bagging Boosting, and Diverse Single Classifiers

Comprehensive Methodologies for Breast Cancer Classification...

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Innovation in Technology (ASIANCON), Asian Conference on

作者： Dhruvi T Avlani Abhijna MB Sai Disha GS Ashwini Kodipalli Trupthi Rao Dept. of Information Science and Engineering Global Academy of Technology Bangalore Karnataka India Dept. of Artificial Intelligence and & Data Science Global Academy of Technology Bangalore Karnataka India

ISBN: (数字)9798350354218

ISBN: (纸本)9798350354225

Breast cancer is one of the most common causes for deaths owed to cancer. It is very crucial and necessary to detect cancer at early phases. There are various Machine Learning techniques and computational techniques available for the aim of diagnosis of breast cancer data. This paper offers a Machine Learning model to perform automated diagnosis for the classification of breast cancer. This method employs single classifier model and bagging boosting for feature selection. Also, four algorithms SVM, Decision Tree, KNN and Logistic Regression which gives the highest accuracy of 88%. The system was trailed on BCGENES dataset. The performance of the system is measured on the basis of accuracy and precision. The incorporation of XAI methods, such as SHAP values and LIME, provides a nuanced understanding of feature importance and individual predictions, elevating model interpretability. The amalgamation of machine learning classifiers, hyperparameter tuning, and XAI techniques contributes to advanced screening tools, fostering trust among clinicians and researchers.

关键词： Support vector machines Technological innovation Machine learning algorithms Accuracy computational modeling Boosting Breast cancer Medical diagnostic imaging Bagging Tuning

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Enhancing Detection of Breast Cancer Through a Novel Framework: A Machine Learning Approach with Explainable AI Interpretation

Enhancing Detection of Breast Cancer Through a Novel Framewo...

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Women in Computing (InCoWoCo), International Conference for

作者： Vidyashree R Ashwini Kodipalli Trupthi Rao Dept. of Computer Science & Engineering Global Academy of Technology Bengaluru Karnataka India Dept. of Artificial Intelligence & Data Science Global Academy of Technology Bangalore Karnataka India

ISBN: (数字)9798331518943

ISBN: (纸本)9798331518950

Breast cancer, the most prevalent cancer among women, significantly contributes to increased mortality rates and ranks as the second leading cause of cancer-related deaths in women. Early detection is crucial for effective treatment, yet traditional diagnostic approaches often involve multiple laboratory tests supervised by medical experts. To mitigate human error and expedite breast cancer detection, there is a pressing need for automated systems capable of accurate and timely diagnosis. This study aims to investigate the application of machine learning (ML) techniques in classifying breast cancer using features from digitized images of fine-needle aspiration (FNA) breast mass samples. Machine learning methods are instrumental in addressing this imperative. The study conducts a comparative analysis of various ML classification methods, including Logistic Regression, Support Vector Machine (SVM), K Nearest Neighbor (KNN), Decision Trees, Bagging-Boosting, and ensemble models, using a breast cancer dataset to establish a novel framework. The performance of each algorithm is assessed based on metrics such as accuracy, precision, recall, and F1 score. The findings underscore the effectiveness of ensemble classifier models, achieving a notable accuracy rate of 98.87%. Additionally, the integration of explainable Artificial Intelligence enhances the interpretability of the models by providing valuable insights into their decision-making processes.

关键词： Support vector machines Measurement Analytical models Accuracy computational modeling Mortality Pressing Nearest neighbor methods Breast cancer Medical diagnostic imaging

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Resource Allocation and Service Provisioning for Softwarized Network Slicing Using Game Theory: A Techno-Economic Approach

Resource Allocation and Service Provisioning for Softwarized...

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IEEE India Conference (INDICON)

作者： Deborsi Basu Samriddha Sanyal Goutam Das Uttam Ghosh Raja Datta G. S. Sanyal School of Telecommunications Indian Institute of Technology Kharagpur India Dept. of Electronics and Electrical Communication Engineering Indian Institute of Technology Kharagpur India Dept. of Computer Science and Data Science School of Applied Computational Sciences MMC TN USA

ISBN: (数字)9798350391282

ISBN: (纸本)9798350391299

Softwarized Network Slicing (SNS) is a forthcoming technological advancement that is going to transform conventional resource allocation techniques. Optimizing the flow of slice resources among the key network players, namely MVNOs (Mobile Virtual Network Operators), MIPs (Mobile Infrastructure Providers), and UEs (User Entities), is a complex and challenging task due to its complicated combinatorial nature. Moreover, maintaining the concurrent quality of service of end-users based on UE fairness is also difficult. Considering these factors, in this work, we have proposed a novel resource allocation strategy using a two-tier matching game between MIPs, MVNOs, and UEs, which not only minimizes the resource allocation cost but also maintains a decent UE satisfaction by providing desired UE SINR. An MILP (Mixed Integer Linear Programming) is formed for cost minimization, and the same is solved using a modified differed acceptance stable algorithm. We have compared our improvement with existing and latest state-of-the-art techniques.

关键词： Costs Network slicing Games Transforms Quality of service Interference Minimization Mixed integer linear programming Resource management Signal to noise ratio

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Automated Detection of Poultry Farms from Aerial Images for Actionable AI System toward Biosecurity Applications

Automated Detection of Poultry Farms from Aerial Images for ...

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2023 IEEE Applied Imagery Pattern Recognition Workshop, AIPR 2023

作者： Li, Yu Das, Rana Duong, Cuong Lim, Teng-Teeh Haithcoat, Timothy Ersoy, Ilker Shyu, Chi-Ren University of Missouri Department of Electrical Engineering and Computer Science ColumbiaMO United States University of Missouri Biological Engineering ColumbiaMO United States University of Missouri Plant Science and Technology Extension ColumbiaMO United States University of Missouri Institute for Data Science and Informatics Columbia United States University of Missouri Institute for Data Science and Informatics Electrical Eng. and Computer Science Dept. ColumbiaMO United States

ISBN: (纸本)9798350359527

Poultry productions have shifted towards larger farms and often cluster in certain regions. However, many of the smaller farms with a considerable amount of production are not considered concentrated animal feeding operation under regulation in many regions, and therefore are not required to obtain a permit. As a result, information of these operations is not being recorded and monitored, contributing to an increased risk for biosecurity issues. The development in computer vision and availability of high-resolution remote sensing imagery allows the possibility for automatic localization and operation estimation of the unregistered farms, filling in the missing information needed for biosecurity risk management. The farm information extracted from images can be incorporated with other available geospatial data, such as road networks, watersheds, etc., to build a comprehensive knowledge base for actionable plans. In the current study, we present a framework for automated localization, analysis, and visualization of poultry farms from remote sensing images. A robust machine learning method was developed to computationally link the visual features from remote sensing imagery to Missouri Department of Natural Resources Concentrated Animal Feeding Operation data, to identify the animal farm type, location, and potential capacity in the region. The developed process can be performed automatically on images of different timestamps to analyze temporal and spatial development of poultry production facilities of a region. This research contributes to the effective management and regulation of poultry and potentially other animal farms, providing insights into farm characteristics and capacities while facilitating informed decision-making. © 2023 IEEE.

关键词： Object detection

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Symptoms-based Classification of Disease Using Various ML Algorithms and Interpretation using LIME and SHAP KERNELS

Symptoms-based Classification of Disease Using Various ML Al...

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Innovation in Technology (ASIANCON), Asian Conference on

作者： Soujanya KJ Ashwini Kodipalli Sanjeev Gosai Sneha BJ Trupthi Rao Dept. of Artificial Intelligence & Data Science Global Academy of Technology Bangalore Karnataka India Dept. of Computer Science & Engineering Global Academy of Technology Bangalore Karnataka India

ISBN: (数字)9798350354218

ISBN: (纸本)9798350354225

Using a variety of machine learning techniques, this research study suggests a unique method for classifying diseases using symptom-based analysis. To improve model transparency and comprehension, the study makes use of the interpretability tools, SHAP kernels and LIME. The research attempts to close the gap between interpretability and prediction accuracy in medical diagnosis by combining two approaches. A thorough assessment of symptom-based categorization is ensured by the use of many machine learning methods. The application of LIME and SHAP kernels aids in the clarification of model predictions and provides information on the primary symptoms that impact illness categorization. This study advances the creation of accurate and comprehensible illness categorization models, promoting confidence in the use of machine learning methods in healthcare.

关键词： Technological innovation Machine learning algorithms Accuracy Machine learning Predictive models Prediction algorithms Vectors Classification algorithms Kernel Diseases

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Detection of PCOS Leveraging Machine Learning and Interpretation Using Explainable AI

Detection of PCOS Leveraging Machine Learning and Interpreta...

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Women in Computing (InCoWoCo), International Conference for

作者： Bindu Madhavi V Asfiya Firdouse Ashwini Kodipalli Dept. of Computer Science and Engineering Global Academy of Technology Bengaluru Karnataka India Dept. of Artificial Intelligence & Data Science Global Academy of Technology Bengaluru Karnataka India

ISBN: (数字)9798331518943

ISBN: (纸本)9798331518950

Polycystic Ovary Syndrome (PCOS) is a widespread endocrine disorder impacting women globally. This research aims to early predict and detect PCOS which is needed to reduce long-term complications. Since it is considered to be a hard-to-diagnose disorder, machine learning is utilized for this purpose. This study leveraged the power of computational algorithms trained on patient data for model prediction where Decision tree with criterion Gini index outperformed with 88.07% accuracy, 85.29% precision, 78.37% recall and 81.69% F1 Score. In addition to this, Bagging and boosting algorithms were used to monitor their performance metrics where Gradient Boost stood out with a remarkable accuracy of 91.74%, 91.00% precision, 97.00% recall and 94.00% F1 Score. By optimizing the chosen parameters through Hyperparameter tuning, a notable increase in the model's accuracy was observed. Besides, three ensemble models were proposed out of which the ensemble classifier ensemble model involving bagging, boosting and single classifiers brought a significant difference of 95.41% accuracy. Additionally, Explainable (XAI) methodologies like LIME (Local Interpretable Model-Agnostic Explanations) and SHAP (SHapley Additive exPlanations) were used for model interpretability.

关键词： Accuracy Machine learning algorithms Ultrasonic imaging computational modeling Medical services Transforms Boosting Prediction algorithms Bagging Tuning

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DeepCare: Deep Learning-Based Smart Healthcare Framework using 5G Assisted Network Slicing

DeepCare: Deep Learning-Based Smart Healthcare Framework usi...

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2022 IEEE International Conference on Advanced Networks and Telecommunications Systems, ANTS 2022

作者： Basu, Deborsi Krishnakumar, Vikram Ghosh, Uttam Datta, Raja Indian Institute of Technology G. S. Sanyal School of Telecommunications Kharagpur India SASTRA Deemed Univ. Dept. of Electronics and Communication Engineering Tamil Nadu Thanjavur India School of Applied Computational Sciences MMC Dept. of Data Science and Computer Science United States Indian Institute of Technology Dept. of Electronics and Electrical Comm. Engg. Kharagpur India

ISBN: (纸本)9781665473408

5G and beyond communication networks require satisfying very low latency standards, high reliability, high-speed user connectivity, more security, improved capacity and better service demands. Augmenting such a wide range of KPIs (Key Performance Indicators) needs a smart, intelligent and programmable solution for TSPs (Telecommunication Service Providers). Resource availability and quality sustainability are challenging parameters in a heterogeneous 5G environment. Programmable Dynamic Network Slicing (PDNS) is a key technology enabling parameter that can allow multiple tenants to bring their versatile applications simultaneously over shared physical infrastructure. Latest emerging technologies like virtualized Software-Defined Networks (vSDN) and Artificial Intelligence (AI) play a pivotal supporting role in solving the above-mentioned constraints. Using the PDNS framework, we have proposed a novel slice backup algorithm leveraging Deep Learning (DL) neural network to orchestrate network latency and load efficiently. Our model has been trained using the available KPIs and incoming traffic is analyzed. The proposed solution performs stable load balancing between shared slices even if certain extreme conditions (slice unavailability) through intelligent resource allocation. The framework withstands service outage and always select the most suitable slice as a backup. Our results show latency-aware resource distribution for better network stability. © 2022 IEEE.

关键词： Deep learning

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