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2nd IEEE International Conference on IoT, Communication and Automation Technology, ICICAT 2024

作者： Gupta, Shashank Sounkhla, Indu Dubey, Ranjeet Kumar Yadav, Mithilesh Kumar Singh Yadav, Sandeep NIT Dept. of Computer Science and Engineering Jalandhar India KIPM Dept. of Computer Science and Engineering Gorakhpur India Buddha Institute of Technology Dept. of Computer Science and Engineering Gorakhpur India

ISBN: (纸本)9798350368109

Internal combustion engine (ICE) vehicles are very polluting and release high nitrogen oxides, carcinogens, and soot into the environment. ICE vehicles are the best choice in this era, but replacing ICE vehicles with electric vehicles (EVs) and automated vehicle control is a potential transition process. The most important turning point in the history of the automobile occurred with the entry of EVs into the vehicle market. The rise of the Internet of Things (IoT) paradigm in electric vehicles (EVs) has changed the way we look at large-scale transportation by making it intelligent and more sustainable. Recently, with the advent of connectivity through IoT platforms, IoT in EVs has improved performance and reduced environmental impact: from any on-off switch and adoption of IoT through our smartphones placed on the charging base. We can implement smart charging solutions for better energy consumption management, reduction of grid load, and battery life enhancement. IoT allows electric vehicles to communicate directly with charging stations and even the power grid, providing a better way to create balanced transportation networks. But implementing IoT in electric vehicles also raises some issues, including data security and user privacy. With all of these issues, there is huge potential in IoT usage with electric vehicles to help produce cleaner transportation and create a more sustainable future. © 2024 IEEE.

关键词： Electric Vehicles Internal Combustion engine Internet of Things

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Ocular Disease Detection using Machine Learning 8

Ocular Disease Detection using Machine Learning

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8th International Conference on Electronics, Communication and Aerospace Technology, ICECA 2024

作者： Bangera, Vaishali Rajashree Bhagawath, Srilaxmi Raja Shetty, Srujana A Spoorthi, B. Nmamit Dept. of Computer Science Nitte Karkala India Sit Dept. of Computer Science Valachil Mangaluru India

ISBN: (纸本)9798350367904

The prevention of visual impairment or blindness is largely dependent on early detection of ocular illnesses, which pose a serious worldwide health burden. Using a carefully selected dataset from Kaggle, this study investigates the use of machine learning techniques for the automated detection of ocular disorders. The dataset is made up of a wide range of retinal images that have been labeled with details on different eye disorders. The goal is to find a deep learning model that is reliable and precise enough to analyze retinal images and categorize them into several disease categories, such as glaucoma, macular degeneration, diabetic retinopathy, and normal conditions. A comparative analysis between different deep learning models like CNN, BCNN, VGG16 and VGG19 is performed in order to find the model that results in higher efficiency, that is, the model that best fits the dataset for multiocular disease detection. The idea of multi-disease detection was also proposed in the year 2021, which set a benchmark by gathering huge amount of relevant data for the study. It differentiated itself from the previous findings that where focused on predicting single ocular disease. Hence this research is a advancement in multi-disease detection where VGG16 proved to be the best fitting model for each label, with efficiency of 9 6 %. © 2024 IEEE.

关键词： Adversarial machine learning

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Prediction of Small Molecules Binding Site with RNA as a Target 15

Prediction of Small Molecules Binding Site with RNA as a Tar...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Majumder, Subhasree Pal, Debnath Indian Institute of Science Bangalore Dept. of Computational and Data Science Bangalore India

ISBN: (纸本)9798350370249

RNA are essential biomolecules and are potent drug targets, as many small RNA molecules are known to play significant roles in many disease pathways. Predicting drug binding sites in RNA molecules is thus an essential step towards RNA-based drug discovery. Several previous works have focussed on sequence-based and structure-based features to predict binding sites in proteins and RNA. The low availability of RNA-small structures is a bottleneck for predictive model performance. We show that using an atomic-level feature set instead of residue-level feature set, increases the dataset size and can help build a promising model using a dataset of smaller size, which is the primary concern in RNA-Small molecule complexes. We focus on a rich set of physio-chemical, structural information, and dynamic features at the atomic level. Lastly we use a voting scheme to predict the small molecule binding sites for RNA. K-Nearest Neighbour (KNN), Multilayer Perceptron (MLP), Decision Tree and Random Forest models performed well with MCC values above 0.5. Among them, Random Forest performed best with an MCC of 0.78, and an accuracy of 0.89. © 2024 IEEE.

关键词： Binding sites

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Comparative Analysis of Feature Selection Methods for Software Bug Classification

Comparative Analysis of Feature Selection Methods for Softwa...

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2024 International Conference on science, Engineering and Business for Driving Sustainable Development Goals, SEB4SDG 2024

作者： Orenyi, Bajeh Amos Oluwambo Tolulope, Olowe Tobi, Asani Emmanuel University of Ilorin Dept. of Computer Science Ilorin Nigeria Landmark University Dept. of Computer Science Omuaran Nigeria

ISBN: (纸本)9798350358155

Software bug classification is a critical task in software engineering aimed at identifying defects early to improve software quality and reliability. Despite its importance, effectively classifying software defects remains a challenge, necessitating the use of advanced techniques such as feature selection. This research presents a comprehensive study on feature selection methods for software defect classification, to evaluate their effectiveness in enhancing classification accuracy. The study investigates the impact of feature selection methods (filter-based, wrapper-based, and embedding) on model performance using classification algorithms such Naïve Bayes, Support Vector Machines, K-Nearest Neighbour, and Random Forest. The studies are carried out using publicly accessible software defect datasets, and conventional evaluation measures such as accuracy, precision, recall, and F1-score are used to evaluate the effectiveness of each feature selection strategy. The findings of the study confirm the effectiveness of ensemble methods in bug severity classification, with Random Forest achieving notable accuracy rates for different datasets. Additionally, the study highlights the superiority of wrapper feature selection techniques over filter methods, demonstrating their ability to select informative features for defect severity classification. The findings of this work give useful information for practitioners in selecting and implementing feature selection approaches when developing defect classification models, ultimately contributing to the enhancement of software quality and reliability. © 2024 IEEE.

关键词： Software quality

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Youth Unemployment Mitigation: Leveraging Machine Learning and Exploratory data Analysis for Evidence-Driven Strategies

Youth Unemployment Mitigation: Leveraging Machine Learning a...

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2023 IEEE Conference on Engineering Informatics, EI 2023

作者： Aarthi, S. Dharmita, A. Pragathi, G. Elakkiya, S. Srm Institute of Science and Technology Dept. of Computer Science Engineering Chennai India Srm Institute of Science and Technology Dept. Computer Science Engineering Specialization with Big Data Analytics Chennai India

ISBN: (纸本)9798350338522

Youth unemployment represents an urgent global socioeconomic challenge. This study combines machine learning and exploratory data analysis to understand and solve this problem comprehensively. Traditional approaches often rely on qualitative assessments and historical data requiring a data-driven transition to more effective solutions. Our approach integrates machine learning algorithms and Exploratory data Analysis techniques to analyze diverse data sets, identify influencing factors, and recommend targeted interventions. The analysis process includes data collection, preprocessing, feature selection, machine learning modeling, and Exploratory data Analysis techniques to understand the dynamics of the Better understanding unemployment among young people. Preliminary results reveal precise predictive patterns and previously unrecognized relationships, providing evidence-based solutions to inform policy and support the economic well-being of younger generations. Further research and validation are needed to increase applicability. © 2023 IEEE.

关键词： data visualization

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AAF-SCM: An Authenticated Framework for Supply Chain Management

AAF-SCM: An Authenticated Framework for Supply Chain Managem...

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2024 IEEE International Conference on Intelligent Systems and Advanced Applications, ICISAA 2024

作者： Roy, Prince Kumar, Rajneesh Morshed, Md Neaz Fairleigh Dickinson University Dept. of Applied Computer Science United States Dept. of Computer Science & Engineering India

ISBN: (纸本)9798331539948

The process of a supply chain involves a series of activities that distribute, assemble, and manage products, transferring them from the supplier to the end consumer. It operates as a complex, interconnected network regulated by supply and demand. Security in supply chain management is a crucial aspect of its overall security measures, focusing primarily on maintaining essential end-to-end protection. Without this, several attacks can be used to manipulate data and valuable information from the entire chain of networks. This paper explains how encrypted intermediates can secure the entire SCM by validating each intermediate using public-private key encryption. We also demonstrate and assess how the proposed AAF-SCM model can aid in tracing security breaches or unauthorized data tampering. We also validate this model against nefarious attacks like Fabrication, Assertion, Deletion, and Modification. Finally, we propose opportunities to develop innovative systems that enhance security through intermediary steps. © 2024 IEEE.

关键词： Supply chain management

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On the Rival Nature of data: Tech and Policy Implications 25

On the Rival Nature of Data: Tech and Policy Implications

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4th ACM Symposium on computer science and Law, CS and LAW 2025

作者： Gordon-Tapiero, Ayelet Ligett, Katrina Nissim, Kobbi Hebrew University Dept. of Computer Science Jerusalem Israel Georgetown University Dept. of Computer Science WashingtonDC United States

ISBN: (纸本)9798400714214

data is often thought of and treated as a non-rival good, which would imply that one person’s use of data does not inherently diminish its availability for others. Building on research in privacy and statistics, we argue that there exist many important settings in which data should be treated as a rival good. Our argument takes into account modern uses of data for statistics, machine learning, and a variety of other purposes, in conjunction with requirements of privacy protection and statistical validity. Excessive sharing or reuse of data about individuals can lead to leakage of sensitive personal information, potentially causing harm to those whose information is included in the data. Overuse of data in statistics or machine learning can lead to overfitting, i.e., models that perform well on training data but poorly on fresh unseen data. Recognizing the rival nature of data offers an opportunity to rethink the way data are managed and used. In an age where the training of AI models generates a massive appetite for data, this perspective has the potential to inform the creation of new regulation and technical infrastructure that will be able to safely and responsibly manage data, track their various uses, and ensure that privacy and statistical usefulness are respected and preserved. We observe that current EU regulation and existing approaches to science seeking to increase opportunities for data-sharing and reuse of data misconstrue the complex nature of data, inadvertently creating risks of privacy harms and overfitting, hence squandering the societal benefits that can be derived from data. Recognizing the rival nature of data has implications for policy and practice. Regulation should address the limitations and risks associated with data reuse and facilitate technological measures to track, analyze, and manage data usage with the goal of ensuring that privacy and statistical validity are maintained. © 2025 Copyright held by the owner/author(s).

关键词： Differential privacy

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Windmill Pitch Prediction And Error Correction With Adaptive Control And Current Generation Analysis Using data Analysis

Windmill Pitch Prediction And Error Correction With Adaptive...

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2023 International Conference on Energy, Materials and Communication Engineering, ICEMCE 2023

作者： Minu, M.S. Rohit Suryaa, S. Jovan Titus, J. Sharan, S. Srm Institute of Science and Technology Dept. of Computer Science Engineering Chennai India Srm Institute of Science and Technology Dept. Computer Science Engineering Specialization with Big Data Analytics Chennai India

ISBN: (纸本)9798350393378

The growing prominence of wind energy as an eco-friendly power alternative amplifies the need for optimal turbine performance. Central to this is the ability to accurately predict and modulate the wind turbine pitch. Our research focuses on integrating adaptive control strategies with advanced power generation analytics to enhance pitch prediction precision. We have processed and analyzed vast amounts of wind data using state-of-the-art data analytics to discern patterns in wind velocity changes accurately. The continuous stream of real-time data is the backbone of our adaptive controls, ensuring perpetual pitch adjustments that optimize energy capture. In tandem with this, we present a thorough analysis linking momentary wind shifts to power production, ensuring consistent and efficient power output. The culmination of these methodologies offers a breakthrough solution in wind energy optimization. Preliminary results highlight the paramount importance of real-time data analytics in revolutionizing green energy advancements. © 2023 IEEE.

关键词： Wind power

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A Serendipity Recommendation Method for Book Categories Using BERT to Strengthen the Web Service of the Book

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Journal of Web Engineering 2025年第2期24卷 199-216页

作者： Kim, Youngmo Kim, Seok-Yoon Park, Byeongchan Dept. of Computer Science and Engineering Soongsil University South Korea

In the field of book search, research on a web service-based user-customized book recommendation system is being conducted to respond to increasingly diverse user requirements. The collaborative filtering algorithm, which is mainly used for book recommendation, has a problem in that it is difficult to reflect the user’s recent interest without considering the changes in preference over time, and the user’s satisfaction decreases because it repeatedly recommends only similar items. In this paper, we propose a book recommendation method using category similarity based on deep learning. The proposed method is to predict books to be used next time by inputting users’ past and current book usage history through BERT, a natural language processing model, and to recommend popular books in other categories with high similarity to the predicted book category in the BERT model to reflect serendipity. This method reflects serendipity, which can lead to users’ recent interests and practical preferences, so that recommendation accuracy and user satisfaction can be satisfied at the same time. © 2025 River Publishers.

关键词： BERT book category recommendation serendipity Web service

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Enhancing Hospital Mortality Prediction for Heart Failure Patients through Hyperparameter Optimization and Interpretability Analysis with LIME and SHAP 1

Enhancing Hospital Mortality Prediction for Heart Failure Pa...

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1st International Conference for Women in Computing, InCoWoCo 2024

作者： Meena, S. Murthy, Monisha Kodipalli, Ashwini Global Academy of Technology Dept. of Computer Science and Engineering Karnataka Bengaluru India Global Academy of Technology Dept. of Artificial Intelligence & Data Science Karnataka Bengaluru India

ISBN: (纸本)9798331518943

Heart failure is one of the primary causes for deaths caused in the hospital. Predicting mortality rate of such patients is extremely important for the efficient use of health care resources. This research aims to estimate the hospital mortality rate of Heart Failure(HF) patients using machine learning(ML). Using computational technique, the obtained model has given the most efficient accuracy. This paper uses the classification model to detect two outcomes (0 and 1, i.e., if the patient will survive or not). The classification models used for the hospital mortality prediction are, single classifiers such as Logistic regression, SVM, KNN, Decision tree (Ginni and Entropy);with Logistic regression obtaining highest accuracy of 88.9%, Bagging and boosting models such as Randomforest, Adaptive boosting, Gradient boosting, XG boosting, Categorical boosting;with CatBoost obtaining highest accuracy of 90.2%. To achieve the best accuracy, ensemble models were used out of which ensemble classifier ensemble gave 91.1% accuracy. To understand the features playing important role in decision making, explainable AI(XAI) is used. LIME and SHAP are considered. LIME generated local explanations, it explained the decision of a complex model for a specific instance or observation. In contrast, SHAP provided global explanations, it explained the overall behavior of the model across all instances. © 2024 IEEE.

关键词： Adaptive boosting

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