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3rd International Conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2023

作者： Ravikumar, R.N. Jain, Sanjay Sarkar, Manash Amity University Rajasthan Dept. of Computer Science and Engineering Jaipur India Atria Institute of Technology Dept. of Computer Science and Engineering Banglore India

ISBN: (纸本)9798350319125

This study centers on enhancing the accurateness of coupon recommendation algorithms within the framework of in-vehicle settings. The In-Vehicle Coupon Recommendation dataset from the UCI Repository was utilized in this study. A range of machine learning algorithms, such as Random Forest, Gradient Boosting, Extra Trees, LightGBM, XGBoost, AdaBoost, Stochastic Gradient Descent (SGD), and CatBoost, were employed for analysis. Among the several algorithms considered, CatBoost exhibited notable performance, with a baseline accuracy of 74%. In order to improve the efficacy of the model, we utilized the 'Synthetic Minority Over-sampling Technique' (SMOTE) to mitigate the issue of class imbalance. This was subsequently followed by a thorough hyperparameter adjustment of the CatBoost algorithm. The improved model demonstrated a significant upgrade, with an accuracy rate of 77%. Additionally, there were noteworthy improvements in precision, recall, and F1 score. The AUC, which represents the Area Under the Receiver Operating Characteristic Curve, saw a notable improvement to 0.82. This signifies a noteworthy enhancement in the capacity to differentiate between positive and negative cases. The results emphasize the efficacy of CatBoost in the context of recommending coupons within vehicles, while also drawing attention to the influence of preprocessing methods and hyperparameter adjustment in enhancing the model's overall performance. This study is a valuable contribution to the domain of personalized advertising in the context of vehicles, providing valuable insights for the advancement of coupon recommendation systems that are more precise and dependable. © 2023 IEEE.

关键词： Coupon Recommendation Hyperparameter Tuning Machine Learning Recommender System SMOTE

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A Comparative Analysis for Stroke Risk Prediction Using Machine Learning Algorithms and Convolutional Neural Network Model 3

A Comparative Analysis for Stroke Risk Prediction Using Mach...

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3rd International Conference on Electrical, computer and Communication engineering, ECCE 2023

作者： Ferdous, Most. Jannatul Shahriyar, Rifat Bangladesh University of Business and Technology Bangladesh University of Engineering and Technology Dept. of Computer Science and Engineering Bangladesh

ISBN: (纸本)9798350345360

A critical, sometimes fatal medical disease called a stroke happens when the blood flow to a portion of the brain is broken off. In the case of stroke, urgent treatment is very essential. Nowadays, stroke is the main cause of death and impairment globally, according to WHO. In this situation, it will be very helpful if we predict the probability of stroke earlier depending on some most important features. Many researchers use different machine learning algorithms for prediction but very few researchers use stacking methods and CNN. The main contribution of this paper is to develop a stacking classifier of ensemble methods and the CNN model. In this paper, data-set is collected from Kaggle. Stroke data is imbalanced. Random oversampling is used for balancing data-set. Then most important features are find out using feature selection method, then applying different machine learning algorithms such as Logistic Regression, Decision Tree Classifier, Support vector machine, Random forest classifier, KNearest neighbour's classifier, Bernoulli Naïve Bayes, Gaussian Naïve Bayes, Stacking of six algorithms (Decision Tree Classifier, Support vector machine, Random forest classifier, KNearest neighbor's classifier, Bernoulli Naïve Bayes, Gaussian Naïve Bayes) and CNN. Then comparing the performances for predicting the probability of stroke during both the training and testing periods. Results show that the Stacking of six algorithms gives the highest accuracy, which is 99.89% for testing and 100% for training. © 2023 IEEE.

关键词： Convolutional neural networks

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Evaluating Ensemble Models on Imbalanced Data Sets: A Comparative Study across Varied Minority Class Ratios 4

Evaluating Ensemble Models on Imbalanced Data Sets: A Compar...

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4th International Conference on Emerging Systems and Intelligent Computing, ESIC 2024

作者： Das, Surajit Nayak, Samaleswari P. Sahoo, Biswajit Nayak, Sarat Chandra Kiit Deemed to Be University School of Computer Engineering Bhubaneswar India Silicon Institute of Technology Dept. of Computer Science and Engineering Bhubaneswar India Gitam Deemed to Be University School of Technology Department of Computer Science and Engineering Hyderabad India

ISBN: (纸本)9798350349856

The complexities of using ensemble models to rectify class imbalance in healthcare datasets are investigated in this study. Using well-known methods such as Random Forest, Gradient Boosting, AdaBoost, CatBoost, LightGBM, XGBoost, BRF and EE we perform an extensive analysis on data-sets that exhibit different levels of class imbalance. The study uses stratified k-fold cross-validation for robust evaluation and the Synthetic Minority Over-sampling Technique (SMOTE) for data augmentation. ROC curves are shown, and performance indicators like as accuracy, precision, recall, F1-score, and area under the curve (AUC) are examined. The results shed light on the complex effects of ensemble models on unbalanced healthcare data-sets and offer directions for further investigation. © 2024 IEEE.

关键词： Adaptive boosting

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World Coin Currency Detection: A Comparative Study of Machine Learning and Deep Learning

World Coin Currency Detection: A Comparative Study of Machin...

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2024 International Conference on Smart Devices, ICSD 2024

作者： Dumka, Ankur Pandey, Arnav Mishra, Amit Kumar Bhaskarrao Bahadure, Nilesh Tripathi, Neha Pandey, Neeraj Kumar Women Institute of Technology Dept. of Computer Science & Engineering Dehradun India St. Joseph's Academy Dehradun India Graphic Era Hill University Dept. of Computer Science & Engineering Dehradun India Symbiosis Institute of Technology Nagpur Campus Pune India Dept. of Computer Science & Engineering Dehradun India

ISBN: (纸本)9798350394412

In the economy where trade and finance are closely linked it is essential to accurately identify and authenticate various currencies, especially in industries such, as banking, tourism and retail. Conventional methods, for identifying coins typically require inspections or simple automated processes, which might take up a lot of time and are susceptible, to mistakes. The advancements, in machine learning (ML) and deep learning (DL) technologies, over the years have revolutionized currency detection providing efficient solutions. This article discusses the evaluation of machine learning and deep learning approaches to identify types of coins. It explores how algorithms, like MobileNetV2, Support Vector Machines (SVM), Random Forest, Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN) perform in distinguishing and classifying coin denominations across currencies. The results of experiments conducted on data sets show how these methods compare in terms of accuracy, effectiveness, and reliability. Furthermore, we examine the advantages and constraints of each approach while suggesting directions for advancing research, in the realm of identifying cryptocurrency. © 2024 IEEE.

关键词： Convolutional neural networks

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Systematic Approach to Analyze The Avast IOT-23 Challenge Dataset For Malware Detection Using Machine Learning 18

Systematic Approach to Analyze The Avast IOT-23 Challenge Da...

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18th IEEE International Conference on Emerging Technologies, ICET 2023

作者： Jahangir, Muhammad Talha Wakeel, Muhammad Asif, Humza Ateeq, Ahsan Mns University of Engineering and Technology Dept. of Computer Science Multan Pakistan

ISBN: (纸本)9798350328172

The integration of the Internet of Things (IoT) becomes more ingrained in our lives, it has gained significant importance for manufacturing companies. By enabling real time monitoring of machine status, evaluating product quality, and observing environmental conditions within factories. IoT empowers managers to make well considered decisions that reduce risks and alleviate losses. However, the growing reliance on IoT devices and services has also given rise to concerns about security and safety due to possible anomalies within IoT networks. Swiftly identifying and resolving these anomalies are crucial to prevent potential harm or losses. The proposed method introduces an innovative approach that leverages machine learning and deep learning techniques for the rapid classification of malicious software attacks on IoT networks. The central goal of this research is to conduct a thorough examination of the Avast IoT-23 dataset with the aim of pinpointing the most effective algorithm in terms of performance and efficiency. In this proposed methodology, the Decision Tree (DT) algorithm stands out as the most efficient and effective choice due to its high accuracy and low time complexity cost. © 2023 IEEE.

关键词： Internet of things

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Cyberbullying Text Identification: A Deep Learning and Transformer-based Language Modeling Approach

EAI Endorsed Transactions on Industrial Networks and Intelli...

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EAI Endorsed Transactions on Industrial Networks and Intelligent Systems 2024年第1期11卷 1-12页

作者： Saifullah, Khalid Khan, Muhammad Ibrahim Jamal, Suhaima Sarker, Iqbal H. Department of Computer Science and Engineering Chittagong University of Engineering and Technology Chittagong4349 Bangladesh Dept. of Information Technology Georgia Southern University StatesboroGA United States Centre for Securing Digital Futures School of Science Edith Cowan University PerthWA6027 Australia

In the contemporary digital age, social media platforms like Facebook, Twitter, and YouTube serve as vital channels for individuals to express ideas and connect with others. Despite fostering increased connectivity, these platforms have inadvertently given rise to negative behaviors, particularly cyberbullying. While extensive research has been conducted on high-resource languages such as English, there is a notable scarcity of resources for low-resource languages like Bengali, Arabic, Tamil, etc., particularly in terms of language modeling. This study addresses this gap by developing a cyberbullying text identification system called BullyFilterNeT tailored for social media texts, considering Bengali as a test case. The intelligent BullyFilterNeT system devised overcomes Out-of-Vocabulary (OOV) challenges associated with non-contextual embeddings and addresses the limitations of context-aware feature representations. To facilitate a comprehensive understanding, three non-contextual embedding models GloVe, FastText, and Word2Vec are developed for feature extraction in Bengali. These embedding models are utilized in the classification models, employing three statistical models (SVM, SGD, Libsvm), and four deep learning models (CNN, VDCNN, LSTM, GRU). Additionally, the study employs six transformer-based language models: mBERT, bELECTRA, IndicBERT, XML-RoBERTa, DistilBERT, and BanglaBERT, respectively to overcome the limitations of earlier models. Remarkably, BanglaBERT-based BullyFilterNeT achieves the highest accuracy of 88.04% in our test set, underscoring its effectiveness in cyberbullying text identification in the Bengali language. Copyright © 2024 K. Saifullah et al., licensed to EAI. This is an open access article distributed under the terms of the CC BY-NC-SA 4.0, which permits copying, redistributing, remixing, transformation, and building upon the material in any medium so long as the original work is properly cited.

关键词： Embeddings

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Human Skin Diseases Detection and Classification using CNN 3

Human Skin Diseases Detection and Classification using CNN

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3rd International Conference on Electrical, computer and Communication engineering, ECCE 2023

作者： Ahmed, Shamim Abiduzzaman, M.D. Rajib, Mahedi Hasan Rahaman, Rafat Hussain, Salam Rashid, Hafsha Binta Wadud, Anwar Hussen Amir-Ul-Haque Bhuiya, T.M. Bangladesh University of Business and Technology Dept. of Computer Science and Engineering Bangladesh

ISBN: (纸本)9798350345360

Recently skin disease has turned into a threat to normal human life when it becomes cancer. If there do any negligible steps during treatment it will become severe and also may cause the reason of human death. So, necessary steps in an early stage of the diagnosis are mandatory to avoid the worst scenario like human death, especially when the disease is Melanoma skin cancer. Because the human death rate of Melanoma skin cancer has become high during this recent era. To detect skin cancer disease there is some common treatment strategy which is done by expert healthcare professional like doctor or dermatologist, as well as these procedures are also very lengthy and time-consuming. Therefore, recently some automated computer vision or image processing with artificial neural network-based methods are introduced but during the implementation level, these methods are highly cost-effective thoughthey provide better accuracy during prediction. In this paper, we proposed a deep learning-based CNN method for classifying skin diseases. To implement our proposed model, we gathered approximately 2102 image datasets from a massive repository of diverse source images, particularly dermatoscopic images of pigmented lesions. Our experiment results show an 89.45 percent accuracy in detecting skin cancer disease. We hope our proposed method will help to diagnose this skin cancer disease more efficient way than other methods that have already been used. © 2023 IEEE.

关键词： Diagnosis

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Secure Prefetching for Secure Cache Systems 57

Secure Prefetching for Secure Cache Systems

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57th Annual IEEE/ACM International Symposium on Microarchitecture, MICRO 2024

作者： Nath, Sumon Navarro-Torres, Agustin Ros, Alberto Panda, Biswabandan Indian Institute of Technology Bombay Dept. of Computer Science and Engineering Mumbai India University of Murcia Computer Engineering Department Murcia Spain

ISBN: (纸本)9798350350579

Transient execution attacks like Spectre and its vari-Ants can cause information leakage through a cache hierarchy. There are two classes of techniques that mitigate speculative execution attacks: delay-based and invisible speculation. Invisible speculation-based techniques like GhostMinion are the high-performing yet secure techniques that mitigate all kinds of spec-ulative execution attacks. Similar to a cache system, hardware prefetchers can also cause speculative information leakage. To mitigate it, GhostMinion advocates on-commit prefetching on top of strictness ordering in the cache system. Our experiments show that the GhostMinion cache system interacts negatively with the hardware prefetchers leading to redundant traffic between different levels of cache. This traffic causes contention and increases the miss latency leading to performance loss. Next, we observe that on-commit prefetching enforced by GhostMinion leads to nerformance loss as it affects the prefetcher timeliness. We perform the first thorough analysis of the interaction between state-of-The-Art prefetching techniques and the secure cache system. Based on this, we propose two microarchitectural solutions that ensure high performance while designing secure prefetchers on top of secure cache system. The first solution detects and filters redundant traffic when updating the cache hierarchy non-speculatively. The second solution ensures the timeliness of the prefetcher to compensate for the delayed triggering of prefetch requests at commit, resulting in a secure yet high-performing prefetcher. Overall, our enhancements are secure and provide synergistic interactions between hardware prefetchers and a secure cache system. Our experiments show that our filter consistently improves the performance of secure cache systems like GhostMinion in the presence of state-of-The-Art prefetchers (by 1.9% for single-core and 19.0% for multi-core for the top-performing prefetcher). We see a synergistic behavior of

关键词： Information leakage

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Benchmark Study on YOLOv8 Variants in Localized Multiclass Fault Detection in PCBs 6

Benchmark Study on YOLOv8 Variants in Localized Multiclass F...

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6th International Conference on Electrical engineering and Information and Communication technology, ICEEICT 2024

作者： Najib, Taeef Muntasir, Fahim Wasi, Wasif Al Wazed Sidetrek Dhaka Bangladesh Brac University Dept. of Computer Science and Engineering Dhaka Bangladesh Rajshahi University of Engineering and Technology Dept. of Electrical and Electronic Engineering Rajshahi Bangladesh

ISBN: (纸本)9798350385779

Detecting surface faults on PCBs is critical for ensuring device dependability and performance. Manual PCB inspection methods like X-ray imaging struggle to ensure quality control due to limitations in speed and defect characterization. However, advanced automated technologies, such as object detection models like YOLOv8, have emerged as a more efficient solution. This study focuses on evaluating the performance of different YOLOv8 variants in identifying and classifying various defects on PCBs. A benchmark of how each modification of the model performs detection and the factors behind were investigated here. It was determined that, YOLOv8 is sensitive to the amount of data it is being trained on, as the larger models tend to overlearn on smaller datasets. YOLOv8-m had the best performance, 97.1% precision, concerning the time taken and epoch count, over others. Large YOLOv8 variants, such as YOLOv8x, are also computationally expensive. The models were trained for 200 epochs with a patience value set at 50. Both increasing the number of data points and the resources will improve the performance and facilitate the overall training procedures. A significant gap in automated electronics production is addressed by this study: precise PCB fault localization for higher reliability of the product. © 2024 IEEE.

关键词： Fault detection

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Predicting Mobile Prices with Machine Learning Techniques

Predicting Mobile Prices with Machine Learning Techniques

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2024 International Conference on Computational Intelligence and Computing Applications, ICCICA 2024

作者： Rana, Deepak Singh Dhondiyal, Shiv Ashish Singh, Sumeshwar Kukreti, Sanjeev Dhyani, Ashish Graphic Era Hill University Dept. Of Computer Science And Engineering Dehradun India Graphic Era Deemed To Be University Dept. Of Computer Science And Engineering Dehradun India Jaypee Institute Of Information Technology Dept. Of Computer Science And Engineering India Graphic Era Deemed To Be University Department Of Hospitality Management Uttrakahnd Dehradun India

ISBN: (纸本)9798350306446

Smartphones are increasingly vital to people on a daily basis. Telephones are utilized in all aspects of life, ranging from personal to professional, due to technological advancements. It serves a function beyond making phone calls. It enables internet connectivity and email reading. when not using the computer. The characteristics of a mobile phone are a crucial consideration when buying *** overall objective of this research is to find the best way to apply machine learning to estimate the retail pricing of smartphones based on their individual specs. Individuals who frequently use their phone are more attentive to selecting features. When purchasing a cell phone, a comparison is done based on the price-performance ratio. Phone features are regarded as performance. This research aims to forecast if mobile phones with certain features are considered economical or *** work is capable of being utilized in various marketing and business contexts to assist in making informed purchasing decisions by maximizing features while minimizing costs. © 2024 IEEE.

关键词： Decision trees

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