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A blockchain-based trustworthy collaborative power trading scheme for 5G-enabled social internet of vehicles

Digital Communications and Networks 2022年第6期8卷 976-983页

作者： Ziming Liu Yang Xu Cheng Zhang Haroon Elahi Xiaokang Zhou College of Information Science and Engineering Northeastern UniversityShenyang110819China College of Computer Science and Electronic Engineering Hunan UniversityChangsha410082China Key Laboratory of Blockchain and Cyberspace Governance of Zhejiang Province Zhejiang UniversityHangzhou310058China Department of Computing Science Umea UniversityUmea90187Sweden Faculty of Data Science Shiga UniversityHikone522-8522Japan RIKEN Center for Advanced Intelligence Project RIKENTokyo103-0027Japan

Social Internet of Vehicles(SIoV)falls under the umbrella of social Internet of Things(IoT),where vehicles are socially connected to other vehicles and roadside units that can reliably share information and services with other social entities by leveraging the capabilities of 5G technology,which brings new opportunities and challenges,e.g.,collaborative power trading can address the mileage anxiety of electric ***,it relies on a trusted central party for scheduling,which introduces performance bottlenecks and cannot be set up in a distributed network,in addition,the lack of transparency in state-of-the-art Vehicle-to-Vehicle(V2V)power trading schemes can introduce further trust *** this paper,we propose a blockchain-based trustworthy collaborative power trading scheme for 5G-enabled social vehicular networks that uses a distributed market mechanism to introduce trusted power trading and avoids the dependence on a centralized dispatch *** on the game theory,we design the pricing and trading matching mechanism for V2V power trading to obtain maximum social *** use blockchain to record power trading data for trusted pricing and use smart contracts for transaction *** simulation results verify the effectiveness of the proposed scheme in improving social welfare and reducing the load on the grid.

关键词： Social internet of vehicles Bl ockchain Collaborative power trading Vehicle-to-vehicle charging 5G

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Multi-Dimensional Spectrum Prediction Method Based on Efficient Adaptive Broad Learning

Multi-Dimensional Spectrum Prediction Method Based on Effici...

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IEEE Conference on Wireless Communications and Networking

作者： Niancong Ji Shufei Wang Yibin Zhang Yang Peng Tomoaki Otsuki Dusit Niyato Guan Gui College of Telecommunications and Information Engineering NJUPT Nanjing China Faculty of Science and Technology Keio University Kanagawa Japan College of Computing and Data Science Nanyang Technological University Singapore

ISBN: (数字)9798350368369

ISBN: (纸本)9798350368376

Multi-dimensional spectrum prediction is essential for spectrum sharing and dynamic spectrum access (DSA), tack-ling spectrum scarcity and improving wireless communication. Traditional methods often use machine learning (ML), which requires manual feature extraction, or deep learning (DL), which demands high computational resources. This paper proposes a lightweight multi-dimensional spectrum prediction model using an adaptive broad learning network (ABLN). The model employs a sliding window to preprocess data and establishes input layers using randomly generated feature and enhancement nodes. The weights of broad learning are determined by solving the pseudo-inverse, and the structure is incrementally extended without retraining, reducing computational complexity. An adaptive node increment module optimizes hyperparameters efficiently. Experimental results demonstrate that ABLN reduces computational overhead while maintaining robust prediction performance across various scenarios.

关键词： Wireless communication Adaptation models Adaptive systems Computational modeling Dynamic spectrum access Training data Manuals Predictive models data models Robustness

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Contrastive Learning for Chest X-ray Classification: A Fusion of Topological data Analysis and ResNet 9

Contrastive Learning for Chest X-ray Classification: A Fusio...

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9th IEEE International Conference on data science in Cyberspace, DSC 2024

作者： Ren, Hao Luo, Zeyu Jing, Fengshi Zhang, Xinyue He, Han Yu, Yonghao Zhao, Dawei City University of Macau Faculty of Data Science China Minzu University of China Beijing China Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan China

ISBN: (纸本)9798350391367

Lung diseases such as pneumonia and tuberculosis present significant global health challenges, leading to millions of deaths annually. Chest X-ray imaging is crucial for diagnosing these conditions, but interpretation often relies on specialized radiologists, creating barriers in resource-limited areas. This study introduces a novel framework integrating topological data analysis (TDA), deep learning with ResNet-50, and contrastive learning to enhance the accuracy and computational efficiency of chest X-ray analysis. TDA captures global topological structures, while ResNet-50 extracts detailed local features. Contrastive learning then optimizes the feature space, enhancing the model's discriminative power and generalization capabilities. Comprehensive evaluations on multiple datasets demonstrate the framework's robustness and effectiveness, showing substantial improvements in diagnostic performance and computational efficiency. These findings suggest that the proposed approach is well-suited for real-time clinical applications, especially in resource-limited settings. Future work will focus on further optimizing the model and exploring its application to other medical imaging modalities, broadening its clinical utility. © 2024 IEEE.

关键词： Contrastive Learning

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A Review of the Effectiveness of Management Information System in Decision Making 1

A Review of the Effectiveness of Management Information Syst...

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1st International Conference on Computer science and Emerging Technologies, CSET 2023

作者： Zeidan Al-Kaseasbeh, Hazem Mohammad Wahsheh, Firas Rashed Mohamad, Fatma Susilawati Jusoh, Julaily Aida Farea, Mazen Mohammed Archana, V.K. Irbid National University Faculty of Business and Financial Science MIS Bi and Data Analysis Department Irbid Jordan Ajloun National University Faculty of Business Department of Management Information Systems Ajloun Jordan UniSZA Faculty of Informatics and Computing Malaysia Al-Madinah International University Faculty of Finance & Administrative Sciences Malaysia Jain Deemed to Be University Bangalore India

ISBN: (纸本)9798350341737

The rapid advancement of technology has revolutionized the way organizations operate, resulting in the widespread adoption of Management Information Systems (MIS) as a crucial tool in decision-making processes. This research paper presents a comprehensive review of the effectiveness of Management Information Systems in facilitating decision-making within various organizational contexts. The paper begins with an exploration of the fundamental role of MIS in collecting, processing, and presenting relevant data to decision-makers. Emphasis is placed on understanding how MIS enables the integration of data from diverse sources and transforms it into actionable insights, empowering managers to make informed and timely decisions. Furthermore, this review delves into the different types of Management Information Systems, including Executive Information Systems (EIS), Decision Support Systems (DSS), and Transaction Processing Systems (TPS). By analyzing case studies and real-world examples, the paper highlights the specific applications and benefits of each system type in enhancing decision-making efficiency and accuracy. To assess the effectiveness of MIS in decision-making, the review examines key performance indicators, such as the speed and accuracy of decision-making processes, resource optimization, and overall organizational performance. The role of MIS in improving strategic planning, risk management, and competitive advantage is also thoroughly investigated. Additionally, the review explores potential challenges and limitations associated with the implementation and utilization of MIS in decision-making. Factors such as data security, system reliability, user training, and resistance to change are addressed, offering insights into strategies for mitigating these obstacles. The paper concludes with a synthesized analysis of the existing literature and identifies gaps for future research in the field of Management Information Systems and decision-making effectiveness

关键词： Decision support systems

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Sustainable Learning of Computer Programming Languages Using Mind Mapping

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Intelligent Automation & Soft computing 2023年第5期36卷 1687-1697页

作者： Shahla Gul Muhammad Asif Zubair Nawaz Muhammad Haris Aziz Shahzada Khurram Muhammad Qaiser Saleem Elturabi Osman Ahmed Habib Muhammad Shafiq Osama E.Sheta Department of Computer Science National Textile University Faisalabad37610Pakistan Department of Data Science University of the PunjabLahore54590Pakistan Department of Mechanical Engineering University of SargodhaSargodha40100Pakistan Faculty of Computing The Islamia University of BahawalpurBahawalpur63100Pakistan College of Computer Science and Information Technology Al Baha UniversityAl Baha1988Saudi Arabia Department of Information and Communication Engineering Yeungnam UniversityGyeongsan38541Korea College of Science Zagazig UniversityZagazig44511Egypt

In the current era of information technology,students need to learn modern programming languages efﬁ*** art of teaching/learning program-ming requires many logical and conceptual *** it’s a challenging task for the instructors/learners to teach/learn these programming languages effectively and efﬁ*** mapping is a useful visual tool for establishing ideas and connecting them to solve *** research proposed an effective way to teach programming languages through visual *** experimental study uses a mind mapping tool to teach two programming environments:Text-based Programming and Blocks-based *** performed the experiments with one hundred and sixty undergraduate students of two public sector universities in the Asia Paciﬁc *** different instructional approaches,including block-based language(BBL),text-based languages(TBL),mind map with text-based language(MMTBL)and mind mapping with block-based(MMBBL)are used for this *** results show that instructional approaches using a mind mapping tool to help students solve given tasks in their critical thinking are more effective than other instructional techniques.

关键词： Text programming blocks programming novice programmer

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Early Diagnosis of Acute Lymphoblastic Leukemia Using YOLOv8 and YOLOv11 Deep Learning Models

arXiv

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arXiv 2024年

作者： Awad, Alaa Aly, Salah A. CSE Dept. E-Japan University of Science and Tech. Alexandria Egypt Faculty of Computing and Data Science Badya University Giza Egypt Computer Science Sect. Faculty of Science Fayoum University Fayoum Egypt

Leukemia, a severe form of blood cancer, claims thousands of lives each year. This study focuses on the detection of Acute Lymphoblastic Leukemia (ALL) using advanced image processing and deep learning techniques. By leveraging recent advancements in artificial intelligence, the research evaluates the reliability of these methods in practical, real-world scenarios. Specifically, it examines the performance of state-of-the-art YOLO models, including YOLOv8 and YOLOv11, to distinguish between malignant and benign white blood cells and accurately identify different stages of ALL, including early stages. Moreover, the models demonstrate the ability to detect hematogones, which are frequently misclassified as ALL. With accuracy rates reaching 98.8%, this study highlights the potential of these algorithms to provide robust and precise leukemia detection across diverse datasets and conditions. © 2024, CC0.

关键词： Lung cancer

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Early Fault Detection in Rolling Element Bearings Using Signal Processing Techniques 21

Early Fault Detection in Rolling Element Bearings Using Sign...

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21st IEEE International Conference on Smart Communities: Improving Quality of Life using AI, Robotics and IoT, HONET 2024

作者： Saeed, Ali Akram, M. Usman Khan, Muazzam A. Afsar, M. Belal Quaid-i-Azam University ICESCO Big Data Analytics and Edge Computing Department of Computer Science Islamabad Pakistan National University of Sciences and Technology Department of Computer and Software Engineering Islamabad Pakistan Otto-von-Guericke University Faculty of Electrical Engineering and Information Technology Maddeburg Germany

ISBN: (纸本)9798350378078

Many studies show that bearings are the most vulnerable components in low-voltage motors. While advanced bearing diagnostic systems exist, their cost can be a barrier for non-critical machinery due to the potential wait time to see a return on investment. This research explores the feasibility of using basic signal processing techniques on vibration data for bearing condition monitoring. The primary goal is to analyze benchmark data from the Case Western Reserve University (CWRU) dataset and establish a baseline performance for this data-driven approach. The results suggest that, with appropriate signal processing, it's possible to achieve early detection of bearing faults, leading to more efficient time-To-fault identification with higher detection accuracies ranging from 83 % to 100 % for each method. © 2024 IEEE.

关键词： Signal modulation

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Enhancing Earthquake Prediction through Hybrid Deep Learning Approach 2

Enhancing Earthquake Prediction through Hybrid Deep Learning...

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2nd IEEE International Conference on Integrated Circuits and Communication Systems, ICICACS 2024

作者： Kandan, M. Sarkar, Swagata Sharma, Meenakshi Rajeswari, V. Poonkuzhali, P. Srimathi, S. SRM Institute of Science and Technology School of Computing Faculty of Engineering and Technology Department of Computing Technologies Kattankulathur India Sri Sairam Engineering College Artificial Intelligence and Data Science Chennai India Global Group of Institute Department of CSE Amritsar India Panimalar Engineering College Information Technology India R.M.D Engineering College Kavaraipettai India Saveetha School of Engineering SIMATS Department of Bio Technology Tamilnadu Chennai India

ISBN: (纸本)9798350317558

Earthquakes, one of the greatest natural catastrophes, kill people and harm the economy. Heavy earthquakes and tsunamis may kill thousands and cost millions. Globally, predicting major earthquakes is important. Because earthquake prediction is difficult, researchers employ machine learning to improve forecasts. Many countries have early earthquake warning systems to save lives. Temporal rather than geographical characteristics dominated previous research. A similarity between earthquake and non-seismic signals makes real-time earthquake detection with cellphones or IoT devices problematic. This system may change entity interactions using ML and an IoT network. EEWS metrics are tracked using IoT. However, ML can assess these actions and determine smart city risk reduction and catastrophe management strategies. A deep learning approach for continuous earthquake forecasting utilising past seismic events is proposed. This research predicts earthquakes by position and depth using machine learning and deep learning. ConvLSTM, a Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN), extracts important waveform elements to make the classifier reliable in earthquake parameters. © 2024 IEEE.

关键词： Convolutional Neural Network Deep Learning Earthquake LSTM Machine Learning

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Suspicious Activity Trigger System using YOLOv6 Convolutional Neural Network

Suspicious Activity Trigger System using YOLOv6 Convolutiona...

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International Conference on Artificial Intelligence in Information and Communication (ICAIIC)

作者： Suryanti Awang Mohd Qhairel Rafiqi Rokei Junaida Sulaiman Faculty of Computing Center of Excellence for Artificial Intelligence & Data Science Universiti Malaysia Pahang Pekan Pahang Malaysia Faculty of Computing Universiti Malaysia Pahang Pekan Pahang Malaysia

Property theft is one of the crimes that increases in which leads to a major concern in Malaysia. Despite of having surveillance cameras (CCTV) everywhere, the crimes keep occur due to the lack of security system. The security system can be developed by utilizing the existence of CCTVs specifically home surveillance CCTV. Therefore, this paper introduces a security system known as Suspicious Activity Trigger System (SATS) that able to automatically trigger an alarm or an alert message whenever suspicious activity is detected from the CCTV video image. The activity will be detected in a video image using Deep Learning technique which is YOLOv6 Convolutional Neural Network (CNN) algorithm. The algorithm will detect an object which is a person in the video and classify it as a suspicious activity or not. If the activity is classified as the suspicious activity, the system will automatically display a trigger message to alert SATS user. The user can therefore take whatever appropriate measure to prevent being a victim. Experiments have been conducted using a dataset taken from Google Open Image. We also implemented the experiments on the self-obtained dataset. Based on the experiment, 92.53% for precision and 96.6% of the accuracy is obtained using this algorithm. Therefore, YOLOv6 can be implemented in the security system to prevent crimes in residency areas.

关键词： Deep learning Limiting Tracking Surveillance Logic gates Cameras Behavioral sciences

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Leveraging Deep Learning for Precise Chronic Bronchitis Identification in X-Ray Modalities

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Computers, Materials & Continua 2025年第4期83卷 381-405页

作者： Fahad Ahmad Saad Awadh Alanazi Kashaf Junaid Maryam Shabbir Asim Ali School of Computing Faculty of TechnologyUniversity of PortsmouthWinston Churchill Ave SouthseaPortsmouthPO12UPUK Portsmouth Artificial Intelligence and Data Science Center University of PortsmouthWinston Churchill AveSouthseaPortsmouthPO12UPUK Department of Computer Science College of Computer and Information SciencesJouf UniversitySakakaAljouf72341Saudi Arabia School of Biological and Behavioural Sciences Queen Mary University of LondonLondonE14NSUK Department of Computer Sciences Bahria UniversityLahorePunjab54700Pakistan

Image processing plays a vital role in various fields such as autonomous systems,healthcare,and cataloging,especially when integrated with deep learning(DL).It is crucial in medical diagnostics,including the early detection of diseases like chronic obstructive pulmonary disease(COPD),which claimed 3.2 million lives in ***,a life-threatening condition often caused by prolonged exposure to lung irritants and smoking,progresses through *** diagnosis through image processing can significantly improve survival *** encompasses chronic bronchitis(CB)and emphysema;CB particularly increases in smokers and generally affects individuals between 50 and 70 years *** damages the lungs’air sacs,reducing oxygen transport and causing symptoms like coughing and shortness of *** such as beta-agonists and inhaled steroids are used to manage symptoms and prolong lung ***,COVID-19 poses an additional risk to individuals with CB due to its impact on the respiratory *** proposed system utilizes convolutional neural networks(CNN)to diagnose *** this system,CNN extracts essential and significant features from X-ray modalities,which are then fed into the neural *** network undergoes training to recognize patterns and make accurate predictions based on the learned *** leveraging DL techniques,the system aims to enhance the precision and reliability of CB *** research specifically focuses on a subset of 189 lung disease images,carefully selected for model *** further refine the training process,various data augmentation and noise removal techniques are *** techniques significantly enhance the quality of the training data,improving the model’s robustness and *** a result,the diagnostic accuracy has improved from 98.6%to 99.2%.This advancement not only validates the efficacy of our proposed model but also represents a significant improvement over existing *** h

关键词： Deep learning chronic obstructive pulmonary disease chronic bronchitis convolutional neural network X-ray images

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