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27th International Symposium on Wireless Personal Multimedia Communications, WPMC 2024

作者： Jhingran, Sushant Chaturvedi, Rekha Bansal, Nidhi Malik, Suraj Sharda University Department of Computer Science and Engineering Greater Noida India Manipal University Jaipur School of Information Security and Data Science Department of Data Science and Engineering Rajasthan Jaipur India Pune India IIMT University Department of Computer Science and Engineering Uttar Pradesh Meerut India

ISBN: (纸本)9798350392319

Since the advent of smartphones, capturing images has become deeply embedded in human behavior, evolving into a fundamental part of daily life. Research into human perception of image quality is crucial as people frequently acquire and interpret images. Automation in image processing systems is vital for quantifying image quality, addressing issues like blurriness, noise, and compression that can significantly degrade the visual experience. While image processing professionals can quickly identify such distortions, integrating human visual perception into automated system design remains challenging. Consequently, machine-based evaluation of image quality is a critical research area. Image quality assessment algorithms aim to approximate human visual judgments. Although deep learning has shown remarkable success in various real-world tasks, such as object detection and image classification, its application in image quality assessment is still evolving. The primary challenge is assessing image quality without explicitly considering human perception, making it a dynamic area of ongoing research. A significant hurdle is the lack of images with quality scores, particularly differential average feedback scores provided by image quality experts on a 1-100 scale. This limitation hinders the development of robust models for image quality assessment, highlighting the need for innovative solutions in this evolving research domain. © 2024 IEEE.

关键词： Convolutional neural networks

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A Robust Indoor Vehicle Localization Strategy based on UWB and INS

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IEEE Transactions on Vehicular Technology 2025年第5期74卷 7292-7302页

作者： Cheng, Long Cui, Chen Zhang, Hao Northeastern University Department of Computer and Communication Engineering Qinhuangdao066004 China Northeastern University Hebei Key Laboratory of Marine Perception Network and Data Processing Hebei Qinhuangdao066004 China

High-precision indoor vehicle localization technology has recently been a research hotspot for indoor location services. This paper investigate the indoor combined positioning technology based on Ultra wide band (UWB) and inertial navigation system(INS), aiming to overcome the limitations of UWB in non-line of sight (NLOS) environment and cumulative errors of INS. For dealing with NLOS errors conveniently, we propose a two-layer NLOS error weakening method. Firstly, k-mediods clustering method is used to preprocess the obtained distance measurements, and some of the measurements containing NLOS errors are replaced with the measurements undisturbed by NLOS transmission. The processed result is processed again with gray Kalman filter, which reduces the process noise and NLOS error. A hybrid Gaussian fitting approach is suggested to determine the propagation conditions and simulate the likelihood of propagation through the residual after calculating the difference between the distance that was filtered and the distance that was measured. The two-sided correction technique changed the findings of the fitting method and the improved particle swarm optimization method. The adaptive Kalman filter was then used to combine the INS and UWB location results to get the final location data. Simulation and experimental results show that the proposed algorithm owns higher localization accuracy and dependability. © 1967-2012 IEEE.

关键词： Inertial navigation systems

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Model-based framework for exploiting sensors of IoT devices using a botnet: a case study with android

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Cyber-Physical Systems 2025年第1期11卷 1-46页

作者： Khaliq, Zubair Khan, Dawood Ashraf Baba, Asif Iqbal Ali, Shahbaz Farooq, Sheikh Umar Department of Computer Science University of Kashmir Jammu and Kashmir Srinagar India AI Thndr Dubai United Arab Emirates Department of Computer Science Tuskegee University TuskegeeAL United States Data Engineering hyke.ai Dubai United Arab Emirates

Botnets have become a severe security threat not only to the Internet but also to the devices connected to it. Factors like the exponential growth of IoT, the COVID-19 pandemic, and the ever-larger number of cybercriminals are incentivising the growth of botnets in this domain. The recent outbreak of botnets like Dark Nexus (derived from Qbot and Mirai), Mukashi, LeetHozer, Hoaxcalls, etc. shows the alarming rate at which this threat is converging. The botnets have attributes that make them an excellent platform for malicious activities in IoT devices used by organisations to safeguard the personal and confidential data of their customers, employees, and business partners. The IoT devices have built-in sensors or actuators that can be exploited to monitor or control the physical environment of the entities connected to them, thereby violating the fundamental concept of privacy-by-design of these devices. In this paper, we design and describe a modular botnet framework for IoT. The framework uses an enhanced centralised architecture associated with a novel ‘Domain Fluxing Technique’. The proposed framework will provide insights into how privacy in IoT devices can be incorporated at design time to check the sensors and actuators in these devices against malicious exploitation, consequently preserving privacy. © 2024 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Internet of things

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Enhancing QSAR Modeling: A Fusion of Sequential Feature Selection and Support Vector Machine 14

Enhancing QSAR Modeling: A Fusion of Sequential Feature Sele...

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14th International Conference on Information and Knowledge Technology, IKT 2023

作者： Khajehgili-Mirabadi, Farzaneh Keyvanpour, Mohammad Reza Alzahra University Data Mining Laboratory Department of Computer Engineering Faculty of Engineering Tehran Iran Alzahra University Faculty of Engineering Department of Computer Engineering Tehran Iran

ISBN: (纸本)9798350349412

Quantitative Structure-Activity Relationship (QSAR) modeling is an approach employed to predict the biological response of chemical compounds by considering their structural attributes. Classification machine learning algorithms can learn patterns and relationships between chemical structure (descriptors) and biological activity from datasets and then use this knowledge to predict active or inactive compounds. This study introduces a new approach that combines Sequential Feature Selection (SFS) with the Support Vector Machine (SVM) algorithm to select the most relevant molecular descriptors for QSAR modeling. SFS and SVM work collaboratively to identify the best subset of descriptors, resulting in improved predictive accuracy. The key steps include selecting an appropriate subset of descriptors using SFS from a larger set, SVM models are built using different subsets of descriptors, and the most accurate model is chosen for final use. As shown by measuring Accuracy and precision, In our study, the SFS-SVM model demonstrated superior performance in QSAR modeling on both DKPES and PubChem datasets. It achieved the highest accuracy and precision among the compared models, with 92.87% accuracy and 86.66% precision on DKPES, and 98.18% accuracy and 98.00% precision on PubChem. This outperformed other models like SFS-LR, Cluster-GA, RF, DT, and SVM, highlighting its efficiency in feature selection and predictive accuracy. © 2023 IEEE.

关键词： Support vector machines

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A Comprehensive Approach to SMS Spam Filtering Integrating Embedded and Statistical Features 13

A Comprehensive Approach to SMS Spam Filtering Integrating E...

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13th International Conference on computer and Knowledge engineering, ICCKE 2023

作者： Hosseinpour, Shaghayegh Keyvanpour, Mohammad Reza Alzahra University Faculty of Engineering Data Mining Laboratory Department of Computer Engineering Tehran Iran Alzahra University Faculty of Engineering Department of Computer Engineering Tehran Iran

ISBN: (纸本)9798350330151

Modern society relies heavily on mobile phones to communicate. One of the most valuable mobile phone services is SMS (Short Message Service), which simplifies communication greatly. There have been spammers who have misused this platform by sending inappropriate messages to users, provoking them and costing them money. Due to imbalanced data, unclear semantics, and the inability to extract sufficient features from short messages, SMS spam can be difficult to filter. While spam messages have been filtered so far using various methods, their accuracy is still a work in progress. This study uses embeddings and TF-IDF to provide more information from short text messages while improving SMS spam filtering accuracy. The proposed approach was tested on a real dataset. Experiments analyzing evaluation parameters demonstrate that this model is effective. © 2023 IEEE.

关键词： Embeddings

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Fuzzy-ID3 Algorithm for Breast Cancer Disease Classification 9

Fuzzy-ID3 Algorithm for Breast Cancer Disease Classification

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9th International Conference on Web Research, ICWR 2023

作者： Alast, Zahra Sadat Hosseini Keyvanpour, Mohammad Reza Alzahra University Data Mining Laboratory Department of Computer Engineering Faculty of Engineering Tehran Iran Alzahra University Department of Computer Engineering Faculty of Engineering Tehran Iran

ISBN: (纸本)9798350399691

In terms of death rates, breast cancer comes in second, among women with cancer. Despite the fact that cancer cells grow in a multistep process involving a number of different types of cells, prevention of breast cancer stays a challenge inside the modern world. As a method of breast cancer detection, this paper proposes ENTROPYMOC strategy, a fuzzy decision tree with a new formula of Entropy. It aims to improve the classification accuracy, precision, recall and F1-Measure of the decision tree by overcoming the limitations of the ID3 algorithm, which is not able to classify continuous-valued data. In the field of machine learning, fuzzy decision trees are becoming increasingly popular. This algorithm reduces the complexity of the logarithmic entropy formula by simplifying the Shannon entropy principle. WBCD (Original), WDBC (Diagnostic) and Coimbra datasets are used to test the improved algorithm. Based on the experimental results, the improved fuzzy-ID3 algorithm outperforms the other four classification algorithms (SVM, Naïve Bayes, Random forest and FId3) in terms of accuracy. In Coimbra dataset, accuracy increased by 3.448%. © 2023 IEEE.

关键词： Classification (of information)

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Spatio-Temporal Graph Neural Networks for Accurate Crime Prediction 13

Spatio-Temporal Graph Neural Networks for Accurate Crime Pre...

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13th International Conference on computer and Knowledge engineering, ICCKE 2023

作者： Roshankar, Rojan Keyvanpour, Mohammad Reza Alzahra University Data Mining Laboratory Department of Computer Engineering Faculty of Engineering Tehran Iran Alzahra University Department of Computer Engineering Faculty of Engineering Tehran Iran

ISBN: (纸本)9798350330151

As a matter of public safety and resource allocation, crime prediction is of paramount importance. As a result of applying data preprocessing techniques and a graph-based approach, this paper presents a crime prediction model. This study shows a comprehensive analysis of crime incidents reported in Chicago over five years, utilizing several preprocessing steps in preparing the dataset. Irrelevant features are eliminated, missing values are handled, and a new feature is extracted. An algorithm based on the k-nearest neighbors method is proposed for representing crime incidents by constructing a graph representation. Each crime incident is connected to its k nearest neighbors based on spatial coordinates. This study compares graph-based machine learning algorithms and conventional approaches, including logistic regression, decision trees, and the recently related work, Spatial-Temporal Meta-path Guided Explainable Crime Prediction (STMEC). The results reveal that the graph-based model demonstrates superior accuracy, recall, and F1 scores compared to traditional models, notably outperforming the recently related work, STMEC. Based on the current study, the graph-based approach effectively captures crime data's spatial dependencies and patterns. Incorporating additional features and fine-tuning hyperparameters of the model can provide valuable insights into crime prevention strategies and urban planning applications. © 2023 IEEE.

关键词： Forecasting

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Bridging the Gap: Evaluating the Symbiotic Relationship of Cloud Computing with data Science and data engineering 3

Bridging the Gap: Evaluating the Symbiotic Relationship of C...

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3rd IEEE Delhi Section Flagship Conference, DELCON 2024

作者： Rajesh Sharma, R. Naikwade, Imamhussain Sungheetha, Akey Adere, Ketema Gandasala, G.S. Pradeep Banshal, Sumit Kumar Alliance University Department of Computer Science and Engineering Bangalore India Alliance University Department of Data Science Bangalore India Adama Science and Technology University Department of Computer Science & Engineering Adama Ethiopia

ISBN: (纸本)9798331518592

This paper examines the convergence of cloud computing, facts science, and facts engineering, providing a primer for college kids getting into those fields. The examine highlights the synergistic courting among those domains, displaying how cloud infrastructure complements collaboration and efficiency. By illuminating those interconnections, the paper presents college students with a holistic view of the current facts landscape, making ready them to leverage current equipment withinside the cloud era. © 2024 IEEE.

关键词： data Science

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Evaluation of Supervised Machine Learning Algorithms for Credit Card Fraud Detection: A Comparison 9

Evaluation of Supervised Machine Learning Algorithms for Cre...

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9th International Conference on Web Research, ICWR 2023

作者： Mirhashemi, Qazaleh Sadat Nasiri, Negar Keyvanpour, Mohammad Reza Alzahra University Faculty of Engineering Data Mining Laboratory Department of Computer Engineering Tehran Iran Alzahra University Faculty of Engineering Department of Computer Engineering Tehran Iran

ISBN: (纸本)9798350399691

Every day the modern world is moving towards digitalization and cashless transactions are becoming more common, credit cards are rapidly becoming more popular. Online and offline purchases using credit cards have become increasingly popular, which results in more fraudulent transactions every day. A large number of credit card fraud incidents occur every year and lead to huge financial losses. accordingly, it could be important to choose the best fraud detection method is essential so that it can detect fraud before criminal consumers a stolen card. To detect fraud, one method is to evaluate historical transaction data, as well as both normal and fraudulent transactions, to obtain usual and fraudulent behavior features by using machine learning techniques. we can use machine learning algorithms to solve this problem if we have access to enough data. In this study, our goal is to compare three algorithms for detecting credit card fraud (Decision Tree, Regression Logistic and Random Forest). we want to use a model that is new and based on a hybrid approach for detecting credit card fraud. According to this study, the proposed model is more capable of identifying fraudulent transactions than previous studies. © 2023 IEEE.

关键词： Decision trees

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Artificial intelligence in physiological characteristics recognition for internet of things authentication

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Digital Communications and Networks 2024年第3期10卷 740-755页

作者： Zhimin Zhang Huansheng Ning Fadi Farha Jianguo Ding Kim-Kwang Raymond Choo School of Computer and Communication Engineering University of Science and Technology BeijingBeijing100083China Beijing Engineering Research Center for Cyberspace Data Analysis and Applications Beijing100083China Faculty of Informatics Engineering Aleppo UniversityAleppoSyria Department of Computer Science Blekinge Institute of Technology37179KarlskronaSweden Department of Information Systems and Cyber Security University of Texas at San AntonioSan AntonioTX78249-0631USA

Effective user authentication is key to ensuring equipment security,data privacy,and personalized services in Internet of Things(IoT)***,conventional mode-based authentication methods(e.g.,passwords and smart cards)may be vulnerable to a broad range of attacks(e.g.,eavesdropping and side-channel attacks).Hence,there have been attempts to design biometric-based authentication solutions,which rely on physiological and behavioral *** characteristics need continuous monitoring and specific environmental settings,which can be challenging to implement in ***,we can also leverage Artificial Intelligence(AI)in the extraction and classification of physiological characteristics from IoT devices processing to facilitate ***,we review the literature on the use of AI in physiological characteristics recognition pub-lished after *** use the three-layer architecture of the IoT(i.e.,sensing layer,feature layer,and algorithm layer)to guide the discussion of existing approaches and their *** also identify a number of future research opportunities,which will hopefully guide the design of next generation solutions.

关键词： Physiological characteristics recognition Artificial intelligence Internet of things Biological-driven authentication

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