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4th Asia Service Sciences and Software engineering Conference, ASSE 2023

作者： Takahashi, Hikaru Qiangfu, Zhao University of Aizu Computer Science and Engineering Department Major in Computer Information System Japan University of Aizu Computer Science and Engineering Department Japan

ISBN: (纸本)9798400708534

Positive and Unlabeled (PU) learning is a learning method which can be applied to various field such as recommendation and big data analysis. A direct method to solve PU learning is transform it into a weighted classification problem. However, previously proposed methods assume a strict condition. In this paper, we first investigate if the condition can be satisfied in real world situations and then propose a normalized weighted method which can relax the difficultly of PU learning. © 2023 ACM.

关键词： Machine learning

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Evaluating the Privacy and Security Implications of Mobile Apps 8

Evaluating the Privacy and Security Implications of Mobile A...

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8th International Conference on Electronics, Materials engineering and Nano-Technology, IEMENTech 2025

作者： Mazumder, Shafiul Islam Shovon, Sazzadul Islam Chowdhury, Md Tanvir Islam, Minhajul Raisa, Habiba Azad Rahman, Habibur Sumon, Monjurul Islam Khan, Md. Khalid Mahbub American International University Department of Computer Science and Engineering Dhaka Bangladesh Central Michigan University Department of Information System Michigan United States East West University Department of Computer Science and Engineering Dhaka Bangladesh Department of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9798331520762

The rapid growth of mobile applications, combined with an increasing reliance on these apps for a variety of purposes, has prompted serious concerns about user privacy and data security. This study aims to assess the privacy practices, data handling methods, and security vulnerabilities of mobile apps in a variety of categories, including social media, e-commerce, health and fitness, and financial management. This study identifies major privacy and security issues by examining privacy policies and terms of service, as well as how these apps handle sensitive data. The study also examines free and premium apps' privacy procedures, taking into account the implications for user data protection. The findings illustrate differences in data handling and security standards across different app categories, highlighting the importance of transparent privacy policies, strong security mechanisms, and limited data sharing to protect user information. The research helps to define standards and best practices for promoting secure app activity, increasing user confidence, and guaranteeing a safer digital environment. © 2025 IEEE.

关键词： Sensitive data

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A novel approach to identify parkinson’s disease and other similar neural stress by analysing keystrokes on modern active devices with ensemble classification

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Multimedia Tools and Applications 2025年 1-40页

作者： Roy, Soumen Roy, Utpal Sinha, Devadatta Pal, Rajat Kumar Department of Computer Science and Engineering University of Calcutta Acharya Prafulla Chandra Roy Siksha Prangan JD-2 Sector - III Saltlake City Kolkata700106 India Department of Computer and System Sciences Visva-Bharati University Santiniketan731235 India

Motor skills (related to the motor nerve) and neurocognitive disorders affect humans’ typing ability to an extent that is noticeable while using a keyboard, smartphone, or other electronic gadgets. These two medical conditions are Parkinson’s disease (PD), caused by malfunctions of the motor nerve, and neurocognitive disorder, caused by a deficiency of organismic responses to stimuli. A mild symptom of PD, change in fine motor skills during typing, is reflected heavily in keystroke patterns during the early stages. Similarly, Emotional stress (ES) expresses a neurocognitive disorder that affects cognitive abilities as well. Early symptoms of this disorder are reflected in the keystroke patterns according to their severity. As there is no such pathological examination, it is challenging to perceive and measure the development of such disorders already developed in human behaviour as a disease. Furthermore, early screening of these diseases is essential for future diagnosis and preventing fatal consequences, since both are progressive illnesses. A modest attempt is made here to detect two such neurodegenerative disorders in humans using the way they type, formally known as Keystroke dynamics (KD). In this study, a bootstrapped-based homogeneous ensemble classification method has been proposed to address the uncertain performance and uneven distribution of classes for the detection of such medical conditions using users’ typing tendencies. For this purpose, two recent benchmark datasets were used for the validation and confirmation of operational improvements of the proposed method, which have been validated qualitatively and quantitatively. As a result, sensitivity/specificity of 0.82/0.78 in detecting PD and 0.98/0.98 in ES have been achieved, which is robust and accurate in a more realistic evaluation. The proposed framework explores the possibilities of implementing it in web-based systems, which has significant benefits. A better diagnosis, early detection at home

关键词： Neurodegenerative diseases

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Sagittarius: Floating Self Navigating Robot for Monitor the Water Quality of Rural Reservoirs 6

Sagittarius: Floating Self Navigating Robot for Monitor the ...

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6th International Conference on Advancements in Computing, ICAC 2024

作者： Pedige, Nishan Ilandara Abeywarna, Thaveesha Amarasinge, Randheera Bandara, Menuka Nawarathna Javakody, Anuradha Gamage, Narmada Sri Lanka Institute of Information Technology Department of Computer System Engineering Malabe Sri Lanka

ISBN: (纸本)9798331517878

This paper proposes an optimal solution for monitoring water quality in Sri Lanka, where reservoirs are essential for daily life and agriculture, especially in rural areas. Despite its importance, 3.6 million communities are affected by severe water quality problems, contributing to a high incidence of chronic kidney diseases. While water purification plants are the only solution, high salinity and hardness often make these sources unsuitable. Many rural reservoirs remain unpurified but could potentially provide safe water if properly monitored and maintained. Yet water sources are frequently contaminated by chemical waste and activities associated with agricultural business but effective solutions for continuous water quality monitoring are lacking. This study introduces an autonomous robot equipped with advanced sensors to monitor critical water quality parameters in real-time. By integrating machine learning for data analysis and prediction, Sagittarius enables continuous assessment of reservoir water quality, providing timely insights for public health protection. Field tests demonstrate a prediction accuracy exceeding 98.5%, supporting proactive water management and monitoring. This system has shown potential to aid in early detection of contamination, informing authorities promptly for preventive actions, and enabling sustainable water usage. This solution can significantly contribute to public health protection and informed water resource management decisions. © 2024 IEEE.

关键词： Reservoirs (water)

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Social Distance and Face Mask Detection using vision-based sensors and IP cameras 3

Social Distance and Face Mask Detection using vision-based s...

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3rd International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies, ICAECT 2023

作者： Vineela, A. Devi, G. Lavanya Deekshit, G. Vinay GMR Institute of Technology Department of Computer Science and Engineering Rajam India Department of Computer Science and System Engineering Vishakhapatnam India

ISBN: (纸本)9781665494007

Healthcare systems all over the world are strained as the COVID-19 pandemic's spread becomes more widespread. The only realistic strategy to avoid asymptomatic transmission is to monitor social distance, as there are no viable medical therapies or vaccinations for it. A unique computer vision-based framework that uses deep learning is to analyze the images that are needed to measure social distance. This technique uses the key point regressor to identify the important feature points utilizing the Visual Geometry Group (VGG19) which is a standard Convolutional Neural Network (CNN) architecture having multiple layers, MobileNetV2 which is a computer vision network that advances the-state-of-art for mobile visual identification, including semantic segmentation, classification and object identification. VGG19 and MobileNetV2 were trained on the Kaggle dataset. The border boxes for the item may be seen as well as the crowd is sizeable, and red identified faces are then analyzed by MobileNetV2 to detect whether the person is wearing a mask or not. The distance between the observed people has been calculated using the Euclidian distance. Pretrained models like (You only look once) YOLOV3 which is a real-time object detection system, RCNN, and Resnet50 are used in our embedded vision system environment to identify social distance on images. The framework YOLOV3 performs an overall accuracy of 95% using transfer learning technique runs in 22ms which is four times fast than other predefined models. In the proposed model we achieved an accuracy of 96.67% using VGG19 and 98.38% using MobileNetV2, this beats all other models in its ability to estimate social distance and face mask. © 2023 IEEE.

关键词： computer vision

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Enhanced MADM Strategy with Heptapartitioned Neutrosophic Distance Metrics

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Neutrosophic Sets and systems 2025年 78卷 74-96页

作者： Eassa, Mohamed Myvizhi, M. Elkholy, Mohamed Abdelhafeez, Ahmed Elbehiery, Hussam Computer Science Department Faculty of Information System and Computer Science October 6 University Giza12585 Egypt Applied Science Research Center Applied Science Private University Amman Jordan Department of Mathematics KPR Institute of Engineering and Technology Tamilnadu Coimbatore India Faculty of Computer Science& Engineering Alamein International University Egypt

In this work, a novel methodological approach to multi-attribute decision-making problems is developed and the notion of Heptapartitioned Neutrosophic Set Distance Measures (HNSDM) is introduced. By averaging the Penta-Partitioned Neutrosophic Distance Measures under particular conditional criteria, the suggested technique ranks the options. Additionally, we explore the fundamental characteristics of HNSDM and provide several illustrative theorems. The Heptapartitioned Neutrosophic Set Distance Measures are formulated based on the Hepta Neutrosophic Set. Lastly, a practical numerical example is provided to illustrate the efficacy of the suggested methodology. © 2025, University of New Mexico. All rights reserved.

关键词： Heptapartitioned Neutrosophic set Heptapartitioned Neutrosophic set and MADM strategy Heptapartitioned set Neutrosophic sine distance measure

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Human arm motion capture system 9

Human arm motion capture system

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9th International Conference on Mathematics and computers in Sciences and Industry, MCSI 2024

作者： Olar, Marius Leonard Leba, Monica Ionica, Andreea University of Petrosani Department of System Control and Computer Engineering Petrosani Romania University of Petrosani Department of Management and Industrial Engineering Petrosani Romania

ISBN: (纸本)9798350377224

The implementation of robotics and human support technologies has opened up new possibilities for recovering the mobility impaired and increasing human productivity in the last few decades. Exoskeletons have been developed as a major component of this progress in order to capture any form of arm motion be it passive or active. Motion capture systems, employing different sensors like IMU sensors, EMG sensors, motion sensors, fiber optic sensors are used to capture arm movements. These sensors provide information on position, movement, muscle contraction, and movement intent of the arm. The use of multiple sensor types allows for redundancy and accurate outcomes. Potentiometer position sensor can be combined with other types of sensors to measure acceleration, orientation, force exerted and arm bendability. The combinations improve accuracy and adaptability for arm motion capture during rehabilitation. Various combinations of adapted sensors can be used for each joint of the hand depending on the peculiarities of application. Sensors enable precise and natural control over recovery devices or exoskeletons which is especially important in terms of rehabilitation process improvement. © 2024 IEEE.

关键词： Exoskeleton (Robotics)

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DALR: A Dynamically Adjustable Linear Regression Model for Handling Stream Data 4

DALR: A Dynamically Adjustable Linear Regression Model for H...

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4th International Conference on computer, Communication, Control and Information Technology, C3IT 2024

作者： Mondal, Rishita Mandal, Sourab Dutta, Paramartha Pal, Arindrajit Visva-Bharati Department of Computer & System Sciences WB Santiniketan India Academy of Technology Department of Computer Science and Engineering Adisaptagram WB India

ISBN: (纸本)9798350389128

In response to the escalating demand for machine learning techniques capable of handling real-time data streams, particularly in applications like stock markets, this research dives deep into the domain of stream regression. The objective is to adeptly navigate challenges posed by evolving data over time, differentiating it from conventional static regression. Existing models, such as Fast and Incremental Model Trees with Drift Detection (FIMT-DD), k-Nearest Neighbours (KNN), and Adaptive Linear Regression (ALR) and some other studies, though competent, exhibit certain limitations like learning based on certain portions of data, using a significantly large memory and not capable of handling each data in every timestamp it arrives. We propose the Dynamically Adjustable Linear Regression Model (DALR) to address these shortcomings. DALR introduces an adaptive regression model that dynamically evolves based on historical knowledge and newly acquired data, providing an innovative solution to the intricacies of dynamic data streams. This endeavor has also highlighted that the DALR algorithm consistently demonstrates strong performance across various datasets according to the RMSE metric, surpassing other state-of -the-art algorithms in most instances. © 2024 IEEE.

关键词： Financial markets

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Two-timescale Derivative Free Optimization for Performative Prediction with Markovian Data 41

Two-timescale Derivative Free Optimization for Performative ...

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41st International Conference on Machine Learning, ICML 2024

作者： Liu, Haitong Li, Qiang Wai, Hoi-To Department of Computer Science ETH Zurich Switzerland Department of System Engineering and Engineering Management The Chinese University of Hong Kong Hong Kong

This paper studies the performative prediction problem where a learner aims to minimize the expected loss with a decision-dependent data distribution. Such setting is motivated when outcomes can be affected by the prediction model, e.g., strategic classification. We consider a state-dependent setting where the data distribution evolves according to a controlled Markov chain. We focus on stochastic derivative free optimization (DFO) where the learner is given access to a loss function evaluation oracle with the above Markovian data. We propose a two-timescale DFO(λ) algorithm that features (i) a sample accumulation mechanism that utilizes every observed sample to estimate the gradient of performative risk, (ii) a two-timescale diminishing step size that balances the rates of DFO updates and bias reduction. Under a non-convex optimization setting, we show that DFO(λ) requires O(1/Ε3) samples (up to a log factor) to attain a near-stationary solution with expected squared gradient norm less than Ε. Numerical experiments verify our analysis. Copyright 2024 by the author(s)

关键词： Prediction models

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Use of Euclidean Distance to Improve Fingerprint Matching Accuracy 4th

Use of Euclidean Distance to Improve Fingerprint Matching Ac...

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4th International Conference on Innovations in Computational Intelligence and computer Vision, ICICV 2024

作者： Bhargava, Neeraj Yadav, Rajendra Choudhary, Usha Department of Computer Science School of Engineering System Sciences MDS University Rajasthan Ajmer India Department of Computer and Communication Engineering Manipal University Jaipur Rajasthan Jaipur India

ISBN: (纸本)9789819769919

Fingerprint authentication is the most sophisticated method of all biometric techniques and has been thoroughly verified through various applications. Fingerprint matching has been done using several fingerprint recognition techniques. The methods for matching fingerprints have changed significantly over time, but Euclidean distance is still widely used since it is efficient and straightforward. Euclidean distance is still used in minutiae-based fingerprint matching to produce accurate results. The computational geometry, image processing, and patter recognition fields all make substantial use of Euclidean distance, a fundamental distance matching approach. The purpose of this work is to highlight the importance of normalization in improving contrast, particularly in noisy fingerprint photographs where accuracy is critical. We analyzed how to make fingerprint matching more robots against a reference template by utilizing Euclidean distance between small spots. We also analyzed algorithm’s effectiveness in producing dependable and accurate fingerprint matching results using MATLAB. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Computational geometry

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