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Learning and Technology Conference (L&T)

作者： Ameera Attiah Mawaddah Alagha Lujain Bukassim Passent El-Kafrawy Computer Science Dept. College of Engineering Effat University Jeddah Saudi Arabia

ISBN: (数字)9798331532215

ISBN: (纸本)9798331532222

The global challenge of diabetes demands innovative approaches for early diagnosis and effective management. This paper investigates the integration of advanced dimensionality reduction and feature selection techniques to improve diabetes prediction models. Utilizing Principal Component Analysis (PCA) and the Artificial Bee Colony (ABC) algorithm, the study focuses on optimizing feature selection to enhance model accuracy, computational efficiency, and interpretability. Employing two diverse datasets—the Behavioral Risk Factor Surveillance System (BRFSS) and the Pima Indians Diabetes Dataset—the research evaluates the performance of Random Forest and Logistic Regression models. Results reveal significant improvements in predictive accuracy and computational efficiency, particularly with ABC-selected features, reducing feature dimensionality while maintaining high accuracy. This study underscores the potential of combining PCA and ABC to revolutionize predictive healthcare analytics, offering robust tools for diabetes management and prevention.

关键词： Dimensionality reduction Accuracy Computational modeling Biological system modeling Medical services Predictive models Feature extraction Diabetes Computational efficiency Principal component analysis

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Analysis of Public Sentiment on Dhaka Metro Rail with Transformer Based Architectures 26

Analysis of Public Sentiment on Dhaka Metro Rail with Transf...

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26th International Conference on computer and Information Technology, ICCIT 2023

作者： Jeba, Samiha Maisha Taharat Aurpa, Tanjim Siyam, Farshid Hossain Khan, Rajib Mansia, Faniyam Maria International University of Business Agriculture and Technology Dept. of Computer Science and Engineering Bangladesh Bangabandhu Sheikh Mujibur Rahman Digital University Dept. of Educational Technology Bangladesh

ISBN: (纸本)9798350359015

Dhaka Metro Rail is a mesmerizing initiative of the Bangladesh Government that has brought comfort to people. This project got a huge appreciation from people for its continuous service. Mass people use the train for rambling, going to the workplace, etc. Again, for some unwanted circumstances, Dhaka Metro Rail gleaned negative criticism from various *** this research, we analyzed public sentiment on Dhaka Metro Rail from popular social media using recent NLP architectures like BERT, XLNet and Electra, where BERT outperformed than other with an accuracy of 91.11% in correctly identifying the sentiments of the public *** sentiment of a comment is identified into predefined categories, such as positive, negative, or neutral. © 2023 IEEE.

关键词： BERT ELECTRA Metro Rail Sentiment Analysis Vader XLNet

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Tweetminds Machine-Learning Based Sentiment Classification of Twitter Data

SSRN

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SSRN 2025年

作者： Venkata, Manjula Devarakonda Revu, Akhil Kumar Gude, Anisha Pampana, Narendra Palli, Bharath Palepu, Iswarya Devi Dept of Computer Science Pragati Engineering College A.P Kakinada533437 India

Sentiment analysis plays a vital role in deriving opinions and sentiments from text data, for applications from social media tracking to customer feedback analysis and to decision-making systems. While more complex models like Support Vector Machines (SVM) are generally more widely used for sentiment classification, the current work defies the assumption that model complexity is necessarily correlated with better performance. Rather, we demonstrate that with proper preprocessing, a less complex model like Logistic Regression can, in fact, perform better than SVM. Our experimental results, based on a well-pre-processed dataset, show that Logistic Regression achieved accuracy of 0.941, precision of 0.938, recall of 0.938, and F1-score of 0.925 consistently beating SVM. These results demonstrate the paramount need for data preprocessing in sentiment classification, illustrating how an optimized data pipeline can greatly enhance the predictive strength of more primitive models without excessive computational burden. This research emphasizes the need to use the appropriate preprocessing methods to achieve maximum efficiency in sentiment analysis models and thereby make machine learning applications more affordable and accessible. © 2025, The Authors. All rights reserved.

关键词： Support vector machines

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On Neural-Network-Based Detection for Hypertensive Subjects Using Classification of Retinal Fundus Photographs 53

On Neural-Network-Based Detection for Hypertensive Subjects ...

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53rd IEEE International Symposium on Multiple-Valued Logic, ISMVL 2023

作者： Sonetsuji, Yuki Isokawa, Teijiro Kamiura, Naotake Tabuchi, Hitoshi University of Hyogo Graduate School of Engineering Dept. of Electronics and Computer Science Himeji Japan Hiroshima University Dept. of of Technology and Design Thinking for Medicine Hiroshima Japan

ISBN: (纸本)9781665464161

In this paper, a method of detecting hypertensive subjects is proposed, using retinal fundus photographs classified by convolutional neural networks (CNNs for short). The proposed method employs Inception-v3 model as a CNN. The data to be presented to the proposed model are prepared from retinal fundus photographs. The scheme of fine tuning is conducted to construct a discrimination model. The model specifies the photographs corresponding to the subjects being in hypertensive states. Experimental results establish that the proposed method can achieves favorable metric values. Especially, Accuracy value achieved by the proposed method is comparatively high for the small scale of dataset, compared with the previously proposed method. © 2023 IEEE.

关键词： Ophthalmology

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Identification of Fraudulent Transactions for Enhanced Credit Card Fraud Detection 4

Identification of Fraudulent Transactions for Enhanced Credi...

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

作者： Gupta, Monica Gayathri, V. Narula, Rachna Kumar, Vijay Kaur, Surinder Singh, Neetu Bharati Vidyapeeth's College of Engineering Dept. of Electronics and Communication Engineering New Delhi India Bharati Vidyapeeth's College of Engineering Department of Computer Science and Engineering New Delhi India Bharati Vidyapeeth's College of Engineering Department of Information and Technology New Delhi India

ISBN: (纸本)9798350387490

With the flourishment of the digital era and the evolution of new techniques and technology, this paper highlights the ever-growing risk of credit card fraud. The advancing technology has provided fraudsters with new and untraceable techniques for scamming genuine users. With the increasing impact of fraud, financial institutions must use machine learning tools for scam detection to provide safety and security to their users' accounts. This paper consists of an interdisciplinary comparative analysis of ML algorithms: Random Forest Classifier, XGBoost, ADABoost, Logistic Regression, Decision Trees, and Artificial Neural Networks. The diverse selection of algorithms aims to determine the most effective algorithm with the highest accuracy score for predicting legitimate and fraudulent transactions. The methodology involves fetching data from an Excel file, training the models, testing them on 20% of the dataset, and finally obtaining results in a Word document, having a confusion matrix and accuracy of each algorithm. By comparing the results of the confusion matrix and accuracy score, the Random Forest Classifier and XGBoost demonstrate the highest accuracy scores, while Logistic Regression yields a lower accuracy score. © 2024 IEEE.

关键词： Logistic regression

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IoT-Based Smart Gauntlet to Assist Physically Challenged and Fragile Mortals

IoT-Based Smart Gauntlet to Assist Physically Challenged and...

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International Conference on Circuit, Power and Computing Technologies (ICCPCT)

作者： Thangam S Gurupriya M Kanderi Johith Kumar Kuthati Shreya Lasya Priya Divakarla Dept. of Computer Science and Engineering Amrita School of Computing Bengaluru India

ISBN: (数字)9798350372816

ISBN: (纸本)9798350372823

Individuals who are bedridden or incapacitated struggle to do particular daily tasks because they have trouble with specific hand movements. Some people can need extra help to do jobs or communicate easily. In this situation, flex sensors and an Arduino microcontroller are two features that make IoT smart gloves useful. When a person moves their fingers, the sensors detect those motions, interpret the gestures, and provide the appropriate output to an LCD monitor. The buzzer will sound, the LED will glow, and the carer will receive an emergency alert in case of an emergency. With the assistance of the smart glove, the user can utilize hand motions to turn on the fan or light.

关键词： Temperature sensors Temperature measurement Pressure sensors Microcontrollers Sensor phenomena and characterization Message services Light emitting diodes

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Recognition of Human Anomalies Using Convolution Neural Network

Recognition of Human Anomalies Using Convolution Neural Netw...

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Advances in Data engineering and Intelligent Computing Systems (ADICS), International Conference on

作者： Teja Parella Balakrishna Ponnam Santhiya P Praveen. A Sujihelen. L dept. of Computer Science and engineering Sathyabama Institute of Science and Technology Chennai Tamilnadu India

ISBN: (数字)9798350364828

ISBN: (纸本)9798350364835

By instantly identifying suspicious occurrences, people, and things, smart CCTV systems greatly improve monitoring capacities. In this research, we investigate how to enhance CCTV video stream anomaly detection using state-of-the-art machine-vision and machine-learning techniques, namely UNET and ALEXNET. In contrast to more conventional methods that depend on supervised learning, this research makes use of uncontrolled and semi-supervised learning techniques to tackle the variety and complexity of anomalies. Using algorithms like UNET and ALEXNET, the system can learn to spot outliers on its own, with little to no help from humans. Because it can tell the difference between normal and abnormal situations, this method improves monitoring efficiency and decreases false alarms. In addition, the system maintains original quality for unusual occurrences while keeping typical video at a lower level, optimizing storage economy. By combining UNET and ALEXNET algorithms, smart CCTV systems may detect suspicious events before they happen, which increases security and allows for faster reactions to any dangers.

关键词： Training System performance Supervised learning Streaming media Semisupervised learning Recording Security

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Iterative Risk Aware PRM Path Planning Algorithm for Autonomous Unknown Environments Exploration 10

Iterative Risk Aware PRM Path Planning Algorithm for Autonom...

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10th International Conference on Automation, Robotics, and Applications, ICARA 2024

作者： Bana, Fatemeh Rekabi Alsayed, Ahmad Krajnik, Tomas Arvin, Farshad Durham University dept. Computer Science Durham United Kingdom University of Manchester dept. Mechanical Engineering Manchester United Kingdom Artificial Intelligence Center Czech Technical University Prague Czech Republic

ISBN: (纸本)9798350394245

Autonomous area exploration is a critical application for various robotic systems, with micro-drones being particularly suited due to their high manoeuvrability and reliability. However, their limited operational time and computational power pose challenges for effective exploration. This paper introduces a risk-aware path-planning algorithm for micro-drones, pri-oritising energy efficiency by minimising travel distance. The algorithm, utilizing Monte-Carlo simulations and a risk-aware Probabilistic Roadmap (PRM) method, discretises the workspace and constructs a topological PRM graph to mitigate collisions. Simulation results demonstrate the algorithm's performance in unknown environments, approaching ideal exploration in known areas. Field experiments with Crazyflie micro-drones validate its practical implementation, enabling micro-drones to navigate unknown environments with an acceptable collision risk for efficient exploration in resource-constrained spaces. © 2024 IEEE.

关键词： Drones

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Blockchain Based Security System for Health Care

Blockchain Based Security System for Health Care

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Contemporary Computing and Communications (InC4), IEEE International Conference on

作者： Ankit Rishipal Singh Dept. of Computer Science & Engineering Guru Jambheshwar University of Science & Technology Hisar India

ISBN: (数字)9798350383652

ISBN: (纸本)9798350383669

Security in healthcare systems based on blockchain technology is the subject of ongoing research. Block Chain technology has the potential to revolutionize many different sectors since it provides a decentralized, incontestable, and publicly verifiable record of every transaction activity. Bitcoin's foundational mechanism is referred to as “block chain technology.” This technology allows for completely decentralized transaction processing. Healthcare aims to diagnose, treat, relieve, and cure any and all bodily and mental health issues, as well as to promote health and prevent sickness. Since all transactions in a blockchain are encrypted, it provides the highest level of security possible. Blockchain networks provide the same assurance of security for medical records. The integrity of the network's ledgers may be checked by checking file signatures on each node to make sure no alterations have been performed. The signature will no longer be valid when the record has been modified. The future of blockchain and big data applications is promising. Big Data technology can manage any kind, amount, or speed of data, while blockchain enhances efficiency across all industries. It has been noticed that medical records are maintained on a blockchain for reasons of security. Proposed system has evaluated healthcare data for security. When compared to the results of cutting-edge research, however, the scalability and adaptability of traditional methods are clearly lacking. Better performance and security utilizing blockchain technology is essential, hence developing such a system is a priority.

关键词： Industries Scalability Medical services Mental health Bitcoin Big Data applications Blockchains

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Unified Face Matching and Physical-Digital Spoofing Attack Detection

arXiv

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arXiv 2025年

作者： Kunwar, Arun Rattani, Ajita Dept. of Computer Science and Engineering University of North Texas Denton United States

Face recognition technology has dramatically transformed the landscape of security, surveillance, and authentication systems, offering a user-friendly and non-invasive biometric solution. However, despite its significant advantages, face recognition systems face increasing threats from physical and digital spoofing attacks. Current research typically treats face recognition and attack detection as distinct classification challenges. This approach necessitates the implementation of separate models for each task, leading to considerable computational complexity, particularly on devices with limited resources. Such inefficiencies can stifle scalability and hinder performance. In response to these challenges, this paper introduces an innovative unified model designed for face recognition and detection of physical and digital attacks. By leveraging the advanced Swin Transformer backbone and incorporating HiLo attention in a convolutional neural network framework, we address unified face recognition and spoof attack detection more effectively. Moreover, we introduce augmentation techniques that replicate the traits of physical and digital spoofing cues, significantly enhancing our model robustness. Through comprehensive experimental evaluation across various datasets, we showcase the effectiveness of our model in unified face recognition and spoof detection. Additionally, we confirm its resilience against unseen physical and digital spoofing attacks, underscoring its potential for real-world applications. © 2025, CC BY.

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