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A Secure Authentication Framework for Consumer Mobile Crowdsourcing Networks

IEEE Transactions on Consumer Electronics 2024年

作者： Aldosary, Abdallah Tanveer, Muhammad Ahmad, Musheer Maghrabi, Louai A. Ahmed, Emad A. Hussain, Amir El-Latif, Ahmed A. Abd Department of Computer Engineering Prince Sattam bin Abdulaziz University Riyadh Wadi Addwasir11991 Saudi Arabia University of Management and Technology Department of Computer Science Lahore54770 Pakistan Jamia Millia Islamia Department of Computer Engineering New Delhi110025 India University of Business and Technology College of Engineering Department of Software Engineering Jeddah Saudi Arabia South Valley University Faculty of Computers and Information Department of Computer Science Qena Egypt Edinburgh Napier University United Kingdom Prince Sultan University EIAS Data Science Lab College of Computer and Information Sciences Center of Excellence in Quantum and Intelligent Computing Riyadh11586 Saudi Arabia Menoufia University Faculty of Science Department of Mathematics and Computer Science Shebin El-Koom32511 Egypt

The Mobile crowdsourcing network (MCN) leverages collaborative intelligence to solve complex tasks through group cooperation. It comprises three main components: the end-user, the service provider, and the mobile user. The end-user requests crowd-sensing and computing services from the service provider, who handles task decomposition, distribution, re-composition, and recommendation. Mobile users perform the crowd sensing and computing tasks. Despite its benefits, MCN faces significant security and privacy challenges. Communications between the service provider and the end-user involve sensitive data, and mobile users and service providers communicate over open channels, making them vulnerable to security threats. To address these concerns, we propose a secure authentication framework for MCN, called SAF-MCN. This framework combines the lightweight cryptographic primitive ' ASCON' with elliptic curve cryptography. SAF-MCN allows the service provider to authenticate mobile users before they join the MCN. Once authenticated, a secure channel is established for encrypted communication over the public Internet. The security of SAFMCN is validated using both formal and informal methods, demonstrating its resilience against potential attacks. The use of Scyther further ensures the security of SAF-MCN. Comparative analysis shows that SAF-MCN is superior in terms of security features, computational efficiency, and communication cost. © 1975-2011 IEEE.

关键词： ASCON Authenticated Encryption Authentication Mobile Crowdsourcing Network Scyther Security

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Realizing Emotional Interactions to Learn User Experience and Guide Energy Optimization for Mobile Architectures 22

Realizing Emotional Interactions to Learn User Experience an...

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Proceedings of the 55th Annual IEEE/ACM International Symposium on Microarchitecture

作者： Xueliang Li Zhuobin Shi Junyang Chen Yepang Liu National Engineering Laboratory for Big Data System Computing Technology Shenzhen University China College of Computer Science and Software Engineering Shenzhen University China Research Institute of Trustworthy Autonomous Systems Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation and Department of Computer Science and Engineering Southern University of Science and Technology China

ISBN: (纸本)9781665462723

In the age of AI, mobile architectures such as smartphones are still "cold machines"; machines do not feel. If the architecture is able to feel users' feelings and runtime user experience (UX), it will accordingly adapt performance/energy to find the optimal system-operating state that consumes the least energy to satisfy users. In this paper, we will utilize users' facial expressions (FEs) to learn their runtime UX. We know that FEs are the natural and direct way for humans to convey their emotions and feelings. Our study reveals that FEs also reflect UX. Our research for the first time quantifies the link between FEs and UX. Leveraging this link, the architecture will be able to use the front camera to see FEs and feel users' UX. Based on UX, the architecture can appropriately provision computing resources. We propose Vi-energy system to realize the above idea. Our evaluation shows that Vi-energy reduces energy consumption by 52.9% at maximum and secures UX.

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FoSoHA-Net: Deep Learning for Accurate Breast Cancer Classification Leveraging Hybrid Attention Mechanisms

FoSoHA-Net: Deep Learning for Accurate Breast Cancer Classif...

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Intelligent Perception and Computer Vision (CIPCV), International Conference on

作者： Daniel Addo Mugahed A. Al-Antari Shijie Zhou Eric Ashalley Gladys Wavinya Muoka Obed T. Nartey School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu China Department of Artificial Intelligence and Data Science AISSLab Sejong University Seoul Republic of Korea University of Electronic Science and Technology of China Chengdu China ChengduUniversity of Technology of China Sino-British Collaborations Erxianqiao Campus Chengdu China

ISBN: (数字)9798350360097

ISBN: (纸本)9798350360103

Breast cancer diagnosis is crucial for timely treatment and management. This study introduces FoSoHA-Net, a novel methodology for precise breast cancer classification using histopathological images. FoSoHA-Net integrates a hybrid attention module (HAM) and depthwise separable convolution to enhance feature extraction and improve classification accuracy. The hybrid attention module combines first-order pooling attention (FoPA) and second-order pooling attention (SoPA) to leverage global and local image information, augmenting the model’s discriminative power. Through comprehensive evaluation of the BreaKHis dataset, FoSoHA-Net demonstrates superior performance in accurately classifying histopathological images across various magnification levels. Comparative analysis against established pre-trained models further validates the effectiveness of FoSoHA-Net in breast cancer classification tasks. Overall, FoSoHA-Net shows promise in advancing breast cancer diagnosis through innovative AI-driven approaches.

关键词： Deep learning Measurement Pathology Accuracy Attention mechanisms Convolution Computational modeling

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Influence of Lung Cancer Model Characteristics on Nanodrugs Tumor Targeting Behavior Based on Convolutional Kernel Extreme Learning Machine Model

Influence of Lung Cancer Model Characteristics on Nanodrugs ...

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Advancement in Technology (ICONAT), International Conference for

作者： Manthan S. Manavadaria Srikanth Chintakindi Rajasree Rajamohanan S Krishna Prasad P. Janardhan Saikumar Neerav Nishant EC Department CSPIT CHARUSAT Changa Petlad Gujarat India Associate Project Manager Cloud Software Group Department of Computer Science (Artificial intelligence and Data Science) Karpagam Academy of Higher Education Coimbatore India Department of Mechanical Engineering Nitte (Deemed to be University) NMAM Institute of Technology Nitte Udupi India Department of ECE Audisankara College of Engineering and Technology Gudur India Department of CSE School of Engineering Babu Banarasi Das University Lucknow India

ISBN: (数字)9798350354171

ISBN: (纸本)9798350354188

One of the main killers on a worldwide basis is cancer. In terms of global persistence, lung cancer stands out among the most frequent cancers. For lung cancer, the standard medical approach includes chemotherapy, surgical removal, and radiation therapy. When compared to other methods, this one isn’t extremely targeted and may even damage surrounding healthy cells. There has been recent recognition of nanotechnology as a possible tool for the treatment and management of lung cancer. Preprocessing, feature selection, segmentation, and model training must be executed in this precise sequence to ensure accuracy. As part of the preprocessing, the proposed approach employ the kernel correlation approach. Splitting the input image into smaller pieces with the goal of reducing the overall representation of the image is called image segmentation. To find features with high predictive power and remove redundant ones, feature selection was done in the training cohort. The model was trained using a Convolutional-KELM. As compared to CNN and ELM, the suggested approach performs better. Following implementation of the technique, the accuracy increased by 94.25%.

关键词： Training Image segmentation Accuracy Lungs Lung cancer Feature extraction Convolutional neural networks Kernel Medical diagnostic imaging Tumors

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Hybrid Optimization Algorithm to Mitigate Phishing URL Attacks In Smart Cities

Hybrid Optimization Algorithm to Mitigate Phishing URL Attac...

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Innovative Practices in Technology and Management (ICIPTM), International Conference on

作者： G. Sujatha Manimuthu Ayyannan S. Gayathri Priya V. Arun A. N. Arularasan M Jogendra Kumar Department of Electronics and Communication Sri Venkateswara College of Engineering Tirupati Andhra Pradesh Senior Software Engineering Manager AI/ML and Data Expert San Francisco Bay Area California USA Department of Electronics and Communication R. M. D. Engineering College Kavaraipettai Tamil Nadu Department of Computer Science &Engineering Madanapalle Institute of Technology Madanapalle Andhra Pradesh Department of Artificial Intelligence &Data Science Panimalar Engineering College Chennai Tamil Nadu Department of Computer Science Koneru Lakshmaiah Education Foundation Vaddeswaram Andhra Pradesh

To improve the detection of phishing sites, this proposal introduces feature weights for intelligent phishing site detection based on hybrid bio-inspired algorithms. The proposed approach uses Gray Wolf Optimization (GWO) and Firefly Algorithm (FF), which examines a wide range of website attributes, to more precisely identify phishing sites. Then employs Artificial Neural Network (ANN) to classify different website elements according to the importance of each component in differentiating between legitimate and phishing websites using bioinspired-based recommended site feature weights. According to experimental findings, the suggested hybrid bioinspired-based feature weighting greatly improved classification accuracy, true positive (TPR), and negative rates (TNR), as well as precision and F1 score. Phishing is an online crime that entails the gathering of private information like passwords, account numbers, and credit card numbers. Attackers use alluring URLs to entice phony website visitors. Recently, Artificial Intelligence-based phishing detection has seen some success, and in this study, ANN was used to detect phishing. This ANN classifier may make phishing websites simpler to spot. However, it has been shown that the effectiveness of detection can be increased by applying a genetic algorithm to improve feature selection.

关键词： Uniform resource locators Smart cities Phishing Biological system modeling Artificial neural networks Feature extraction Classification algorithms

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Enhancing Alzheimer Disease Diagnosis: Integrating Gabor Convolutional Neural Network with Conventional CNNs

Enhancing Alzheimer Disease Diagnosis: Integrating Gabor Con...

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Intelligent Perception and Computer Vision (CIPCV), International Conference on

作者： Daniel Addo Mugahed A. Al-Antari Shijie Zhou Eric Ashalley Gladys Wavinya Muoka Obed T. Nartey School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu China Department of Artificial Intelligence and Data Science AISSLab Sejong University Seoul Republic of Korea Institute of Fundamental and Frontier Science University of Electronic Science and Technology of China Chengdu China Erxianqiao Campus ChengduUniversity of Technology of China Sino-British Collaborations Chengdu China

ISBN: (数字)9798350360097

ISBN: (纸本)9798350360103

Alzheimer’s disease (AD) presents a formidable challenge in healthcare, characterized by its progressive nature and the absence of definitive diagnostic markers. This study introduces an innovative method to enhance AD detection by integrating advanced technology with established techniques. We have developed a hybrid model that combines Gabor Convolutional Neural Networks (GCNN) with traditional Convolutional Neural Networks (CNNs) to improve feature extraction from MRI images, thereby achieving a more precise diagnosis of AD. The efficacy of our model is evidenced by its high-performance metrics: an accuracy of $98.58 \%$, a sensitivity of $\mathbf{9 8 . 1 7 \%}$, a precision of $97.11 \%$, and robust empirical support. This approach benefits from the use of augmented data and the unique capabilities of GCNN. Comprehensive evaluations, including confusion matrix analysis and ROC curve assessments, confirm the model’s proficiency in accurately classifying various stages of dementia. By enhancing diagnostic accuracy, this innovative approach holds the potential to improve patient outcomes, facilitate better care, and enable more timely interventions in the treatment of Alzheimer’s disease.

关键词： Analytical models Accuracy Sensitivity Computational modeling Merging Predictive models data models

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AutoLog: A Log Sequence Synthesis Framework for Anomaly Detection

AutoLog: A Log Sequence Synthesis Framework for Anomaly Dete...

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IEEE International Conference on Automated software engineering (ASE)

作者： Yintong Huo Yichen Li Yuxin Su Pinjia He Zifan Xie Michael R. Lyu Department of Computer Science and Engineering The Chinese University of Hong Kong Hong Kong China School of Software Engineering Sun Yat-sen University Zhuhai China School of Data Science The Chinese University of Hong Kong Shenzhen (CUHK Shenzhen) China Huazhong University of Science and Technology Wuhan China

The rapid progress of modern computing systems has led to a growing interest in informative run-time logs. Various log-based anomaly detection techniques have been proposed to ensure software reliability. However, their implementation in the industry has been limited due to the lack of high-quality public log resources as training datasets. While some log datasets are available for anomaly detection, they suffer from limitations in (1) comprehensiveness of log events; (2) scalability over diverse systems; and (3) flexibility of log utility. To address these limitations, we propose AUTOLOG, the first automated log generation methodology for anomaly detection. AUTOLOG uses program analysis to generate runtime log sequences without actually running the system. AUTOLOG starts with probing comprehensive logging statements associated with the call graphs of an application. Then, it constructs execution graphs for each method after pruning the call graphs to find log-related execution paths in a scalable manner. Finally, AUTOLOG propagates the anomaly label to each acquired execution path based on human knowledge. It generates flexible log sequences by walking along the log execution paths with controllable parameters. Experiments on 50 popular Java projects show that AUTOLOG acquires significantly more (9x-58x) log events than existing log datasets from the same system, and generates log messages much faster (15x) with a single machine than existing passive data collection approaches. AUTOLOG also provides hyper-parameters to adjust the data size, anomaly rate, and component indicator for simulating different real-world scenarios. We further demonstrate AUTOLOG's practicality by showing that AUTOLOG enables log-based anomaly detectors to achieve better performance (1.93%) compared to existing log datasets. We hope AUTOLOG can facilitate the benchmarking and adoption of automated log analysis techniques.

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Prediction of Hypertension Disease Using Machine Learning Techniques: Case Study from Jordan

Prediction of Hypertension Disease Using Machine Learning Te...

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Information Technology (ACIT)

作者： Raed Alazaidah Ghassan Samara Anwar Katrawi Wael Hadi Maram Y. Al-Safarini Farah Al-Mamoori Ali Elrashidi Muhyeeddin Alqaraleh Haneen Alzoubi Department of Data Science and AI Faculty of Information Technology Zarqa University Zarqa Jordan Department of Computer Science Faculty of IT Zarqa University Zarqa Jordan Information Security Department Faculty of IT Petra University Amman Jordan Department of Pharmaceutical Faculty of Pharmacy Zarqa University Zarqa Jordan Department of Electrical Engineering University of Business and Technology Jeddah Saudi Arabia Department of Software Engineering Faculty of IT Zarqa University Zarqa Jordan Department of Computer Science Facultty of Information Technology Applied Science Private University Amman Jordan

ISBN: (数字)9798331540012

ISBN: (纸本)9798331540029

Hypertension is one of the most common diseases in Jordan. It is one of the main reasons of death among Jordanian adult citizens. Worldwide, nearly 13% of all deaths are due to Hypertension with nearly 8 million deaths yearly. Hence, this research is interested in the early prediction of this disease among Jordanian people. To achieve this main objective, Machine Learning (ML) is utilized through a large number of classification models and considering five well-known evaluation metrics. These classification models have been trained on a primary dataset that has been collected for the purpose of the research. The results revealed that Random Forest, Random Committee, and MultilayerPerceptron are the best classification models to handle the task of the early prediction of Hypertension in Jordan.

关键词： Hypertension Measurement Predictive models Information technology Random forests Diseases

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SPDM: Spatiotemporal-Periodic Diffusion Model for Multivariate Time Series Imputation

SPDM: Spatiotemporal-Periodic Diffusion Model for Multivaria...

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International Conference on Computer Supported Cooperative Work in Design

作者： Qia Zhang Renfang Wang Hong Qiu Xiufeng Liu Xu Cheng College of Information Technology Shanghai Ocean University Shanghai China College of Big Data and Software Engineering Zhejiang Wanli University Ningbo China Department of Technology Management and Economics Technical University of Denmark Kgs. Lyngby Denmark School of Computer Science and Engineering Tianjin University of Technology Tianjin China

ISBN: (数字)9798331513054

ISBN: (纸本)9798331513061

This paper presents SPDM, an innovative spatiotemporal-periodic diffusion model for multivariate time series interpolation, which addresses the challenges of spatiotemporal dependency and periodicity inherent in real-world datasets. Unlike prior methods, SPDM integrates conditional features capturing spatiotemporal correlations and geographic relationships, enhancing the model's ability to account for complex interdependencies within time series data. A noise prediction module, leveraging Fast Fourier Transform, decomposes time series into periodic components, thereby enabling the model to capture both intra- and inter-period dynamics and inter-channel correlations effectively. Experimental results on multiple industrial datasets show that SPDM outperforms state-of-the-art methods across various missing data scenarios, highlighting its robustness and effectiveness. This work establishes a new approach to time series interpolation by combining conditional information construction with periodicity-aware diffusion modeling, offering promising insights for further applications in time series analysis.

关键词： Interpolation Correlation Fast Fourier transforms Time series analysis Noise Predictive models Diffusion models Imputation Robustness Spatiotemporal phenomena

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G-Bot: Revealing Government Programs with Smarted Assistance

G-Bot: Revealing Government Programs with Smarted Assistance

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Recent Trends in Microelectronics, Automation, Computing and Communications Systems (ICMACC), International Conference on

作者： A. Ramathilagam Kavitha Gunasekaran Elamparithi Pandian M. Satheesh Kumar D. David Neels Ponkumar R. Saravanakumar Department of Computer Science and Engineering P.S.R. Engineering College Sivakasi Tamil Nadu India Department of Artificial Intelligence and Data Science Ramco Institute of Technology Rajapalayam Tamil Nadu India Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Tamil Nadu India Department of Electronics and Communication Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Tamil Nadu India Iconix Software Solution Tirunelveli Tamil Nadu India

ISBN: (数字)9798350366570

ISBN: (纸本)9798350366587

Residents have had trouble getting accurate state program information for years. G-bot's innovative technology employs NLP to link customers to state-level policies and activities to bridge this knowledge gap. G-bot's core NLP module is taught to deliver reliable and trustworthy information to help people make healthy and progressing decisions. G-bot provides real-time, reliable data to promote public awareness, transparency, and involvement in government processes. Users may explore the enormous state program and regulatory information using G-bot's simple interface. G-bot's Flask integration helps users locate vital information. G-bot enhances user-centric apps beyond information retrieval. AI-powered language processing ushers in a new era, letting individuals shape their communities. G-bot uses NER to identify government entities. This tool simplifies identifying organizations, programs, laws, agencies, and people, improving search results and customizing insights for better decision-making.

关键词： Accuracy Shape Government Natural languages Decision making Market research Information retrieval Real-time systems Microelectronics Reliability

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