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A New Alliance of Machine Learning and Quantum Computing: Concepts, Attacks, and Challenges in IoT Networks

IEEE Internet of Things Journal 2025年第12期12卷 18865-18886页

作者： Rishiwal, Vinay Agarwal, Udit Yadav, Mano Tanwar, Sudeep Garg, Deepak Guizani, Mohsen MJP Rohilkhand University Department of Computer Science and Information Technology Bareilly India Bareilly College Bareilly Department of Computer Science India Nirma University Institute of Technology Department of Computer Science and Engineering Gujarat Ahmedabad382481 India SR University School of Computer Science and Artificial Intelligence Telangana- Warangal506371 India Abu Dhabi United Arab Emirates

The Internet of Things (IoT) is a constantly expanding system connecting countless devices for seamless data collection and exchange. This has transformed decision-making with data-driven insights across different domains. However, challenges arise concerning security and computational limitations. To strengthen IoT against cyber threats and optimize resource usage, combining Quantum Computing with Machine Learning (ML) is a promising approach. ML enables computers to learn from data and detect patterns without explicit programming. By leveraging ML algorithms, vast datasets from IoT devices can be analyzed, identifying anomalies and forecasting potential security breaches. Yet, conventional ML algorithms may need help with the complexity and scale of IoT data. Quantum Computing, based on quantum mechanics, offers unparalleled computational speed and scale. Quantum ML algorithms can quickly analyze IoT datasets, identifying patterns and potential threats. This study examines the ideas behind ML, quantum computing, and their potential collaboration within IoT networks. The research focuses on the possibility of improving the security of IoT networks by integrating quantum computing approaches with ML. It also addresses the challenges and limitations of integrating ML and quantum computing in the context of IoT networks. These obstacles include hardware constraints, algorithm complexities, and the need for specialized knowledge. © 2025 IEEE.

关键词： Quantum computers

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AquaticSafe Sentinel: An Edge-Based IoT-Powered Predictive System for Continuous Dissolved Oxygen Surveillance in Aquaculture 13

AquaticSafe Sentinel: An Edge-Based IoT-Powered Predictive S...

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13th IEEE International Conference on Communication Systems and Network Technologies, CSNT 2024

作者： Taghizadeh, Seyedreza Fardarani, Zahra Nooraninejad Shiraz University Engineering and Information Technology Department of Computer Science Shiraz Iran

ISBN: (纸本)9798350305463

Aquaculture stands as a flourishing industry, serving as a significant financial resource for many. However, it encounters various challenges such as environmental degradation and disease outbreaks. Consequently, maintaining water quality in aquaculture systems becomes imperative. Dissolved oxygen (DO), a key indicator of oxygen availability for fish respiration, emerges as a crucial measure of water quality. In this research, we propose AquaticSafe Sentinel, an innovative IoT-backed predictive system designed for edge-based continuous monitoring of DO and real-time alerting in aquaculture. Our two-phase framework utilizes both edge computing and cloud computing to swiftly and reliably predict DO levels based on environmental and biological characteristics. Phase one involves making ensemble forecasts of DO levels at the cloud layer using a combination of three machine learning approaches, namely Linear Regression, Random Forest, and AdaBoost. Then phase two involves employing the results of Linear Regression at the edge of the network to make real-time decisions about DO levels, and triggering alerts if necessary. Subsequently, the system awaits a more refined conclusion from the combination technique at the cloud layer. We conduct a comparative analysis with various baseline methods, demonstrating that our framework surpasses them in terms of prediction accuracy and computing efficiency. Additionally, we illustrate how our framework effectively minimizes false alerts, achieving a balance between speed and accuracy to deliver timely solutions. © 2024 IEEE.

关键词： Edge computing

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3D Pedestrian Detection Based on Pointpillar: SelfAttention-pointpillar 9

3D Pedestrian Detection Based on Pointpillar: SelfAttention-...

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9th International Conference on Intelligent Informatics and BioMedical sciences, ICIIBMS 2024

作者： Liu, YunXiang Pan, Xun Zhu, Jianlin Shanghai Institute of Technology School of Computer Science and Information Engineering Shanghai China

ISBN: (纸本)9798350363043

With the rapid development of autonomous Vehicls technology, the detection of surrounding pedestrians, vehicles, Cyclists and other targets by autonomous vehicles is an indispensable technology, especially for pedestrian detection. Therefore, there aremore and more related algorithmms and network models based on target recognition. In recent years, many scholars have stagnated in the process of discovering new algorithms and models and have rarely improved (such as SECOND, PointRCNN, PointPillars, etc.) These classic models, due to the different configuration environments of these model codes and the need to redownload each time you want to run a new model, are very time consuming and energy consuming. In order to solve these difficulties, we chose to use the OpenPCDet target detection framework to improve these models. This framework integrates all the above original object detection models to facilitate us to improve and compare the indicators between the models, and in the comparison of the results of the original model built in the OpenPCDet framework, it is found that the PointPillars modelusing 3D single-stage object detection is the most suitable for autonomous Vehicles. The recognition speed of the original PointPillars for vehicles, pedestrians and other objects can fully meet the use of autonomous Vehicles technology, but the accuracy of object recognition, especially in pedestrian detection, needs to be improved. In this regard, we propose a SelfAttention-PointPillars model. Based on the architecture of the PointPillars model and the idea of self-attention, we use our own pillar amount and modify the original backbone structure into our own self-attention network to improve the accuracy of identifying target pedestrians. We also improved the original L1 loss function into a faster weighted L2 function and we also replaced the activation function with the more efficient LeakyRelu function. Therefore, this paper mainly introduces the OpenPCDet target detect

关键词： Object detection

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Research on Public Opinion Sentiment Analysis Methods for Social Networks 9

Research on Public Opinion Sentiment Analysis Methods for So...

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9th International Conference on Intelligent Informatics and BioMedical sciences, ICIIBMS 2024

作者： Xiao, Lizhong Yang, Xinglan Shanghai Institute of Technology School of Computer Science and Information Engineering Shanghai China

ISBN: (纸本)9798350363043

In the task of public opinion sentiment analysis, short-text sentiment analysis and public opinion network sentiment evolution models play a crucial role. This paper provides an in-depth discussion of public opinion sentiment analysis, which is divided into two categories: short-text sentiment analysis and public opinion network sentiment *** based on sentiment analysis are further discussed from aspects such as public opinion texts and the impact of public opinion networks, followed by an introduction to datasets construction and model evaluation. Finally, an outlook on future research directions is provided. © 2024 IEEE.

关键词： Network Public Opinion Sentiment Propagation Short-Text Sentiment Analysis

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Motion path planning method based on map reference points

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Multimedia Tools and Applications 2024年 1-21页

作者： Yu, Wenbin Fan, Cheng Zhang, Chengjun Chen, Yadang Li, Yangsong Zhang, Yifan Yin, Na Cen, Xiaolin School of Software Nanjing University of Information Science and Technology Jiangsu Nanjing210044 China School of Computer Science Nanjing University of Information Science and Technology Jiangsu Nanjing210044 China Wuxi Institute of Technology Nanjing University of Information Science and Technology Jiangsu Wuxi214000 China Nanjing University of Information Science and Technology Jiangsu Nanjing210044 China School of Computer Science Northeastern University Boston02101 United States Computer Science and Engineering The University of New South Wales SydneyNSW02101 Australia

In recent years, significant progress has been made in the development of the Rapidly-exploring Random Trees (RRT) algorithm, a probabilistic sampling-based search approach. This algorithm effectively addresses complex challenges in path planning within high-dimensional spaces characterized by intricate constraints. However, the RRT algorithm faces certain limitations, including sluggish computational performance in handling intricate calculations, susceptibility to becoming trapped in confined path regions, and difficulties in generating randomized paths across a given map. To overcome these issues, this study introduces a motion path planning method based on map reference points. Initially, the algorithm selects strategic map reference points, followed by the utilization of an enhanced RRT algorithm for fundamental path selection. Real-world scenario data is collected to empirically evaluate the algorithm’s performance. The outcomes of the experiments reveal that, within the same experimental setting, the proposed method outperforms conventional motion path planning algorithms by effectively circumventing obstacles. Furthermore, it substantially reduces the resource wastage caused by global random sampling in the RRT algorithm, leading to expedited path planning and improved pathfinding success rates. Additionally, the proposed approach enhances the RRT algorithm’s adaptability to some extent, particularly in negotiating narrow passages and intricate environments. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Decision trees

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Data expansion method based on multi-angle SAR image fusion 4

Data expansion method based on multi-angle SAR image fusion

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4th International Conference on Signal Image Processing and Communication, ICSIPC 2024

作者： Liu, Rong Yang, Xiyu Yang, Tingting School of Computer Science and Information Engineering Hefei University of Technology Hefei China

ISBN: (纸本)9781510682467

Based on the characteristics of synthetic aperture radar (SAR) images, this paper designs a lightweight generative adversarial network (GAN) model that uses dual-channel mode to simultaneously read two SAR images from different angles and generate new images containing multi-angle image information to achieve data expansion. This model uses an attention mechanism module to enhance the expressiveness of key features and improve the quality of generated images. Images synthesized by this method incorporate information from multiple angle views and contain more detailed target features. Finally, experiments show that the fused image obtained by this method has high information entropy and is highly similar to the source image. Additional, the effectiveness of the data expansion is proved by using the single shot multibox detection (SSD) framework. © 2024 SPIE.

关键词： Generative adversarial networks

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LiDAR 3D object detection based on improved PillarNet 9

LiDAR 3D object detection based on improved PillarNet

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9th International Conference on Intelligent Informatics and BioMedical sciences, ICIIBMS 2024

作者： Huang, Xinde Shanghai Institute of Technology School of Computer Science and Information Engineering Shanghai China

ISBN: (纸本)9798350363043

To address the limitations of current methods in detecting small objects, such as pedestrians and cyclists, within autonomous driving scenarios, we propose a novel 3D object detection algorithm based on an improved PillarNet architecture. These limitations often manifest as reduced recognition accuracy and increased susceptibility to false positives and negatives. The proposed method helps to capture key contextual information and spatial relationships between point cloud columns by adding a global attention mechanism to the pair. In addition, the addition of residual convolution helps to enhance the feature extraction capability of the network This comprehensive strategy expands the receptive field for point cloud features and bolsters the model's robustness in recognizing small objects within intricate environments. The effectiveness of the proposed method is validated through evaluations on the KITTI dataset. Compared to the baseline PillarNet, our improved algorithm achieves a 2.5% increase in the average detection accuracy for the cyclist category, demonstrating the efficacy of our approach. © 2024 IEEE.

关键词： Object detection

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Steel Surface Defect Detection Algorithm Based on Improved YOLOv8 21

Steel Surface Defect Detection Algorithm Based on Improved Y...

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21st International Conference on Networking, Sensing and Control, ICNSC 2024

作者： Che, Jie Yao, Xinrui Wang, Yuqi Li, Bohan Xu, Hangrui Liu, Jian School of Computer Science and Information Engineering Hefei University of Technology Hefei China

ISBN: (纸本)9798350365221

Surface defect detection technology is a vital component of the steel industry that has garnered significant attention from the academic community in recent times. While modern methods with deep learning-based object detection provide better detection accuracy than traditional approaches, production requirements are often not met by their inference time. This study suggested an improved method based on the industry-leading object detection algorithm YOLOv8 to address this issue and effectively detect steel surface defects while keeping a reasonable balance between speed and accuracy. First, a Stem module based on YOLOv8s is added to downsample the image and lessen the effect of redundant features in the original image. In the meantime, the backbone network gains a large kernel depthwise convolution module called InceptionNeXt+, which improves the model's ability to extract features and has a larger effective receptive field. Lastly, the base model's SPPF module is changed to a RsimSPPF module, which increases the model's accuracy and speeds up inference. The improved YOLOv8 model confirms the efficacy of the improved method with experimental results on the NEU-DET dataset achieving a mAP of 76.9%, increasing by 3.3 compared to the baseline model. © 2024 IEEE.

关键词： Steelmaking

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Intelligent Cost-Effective Electric Vehicle Charging Solutions for Cities and Highway Networks 17

Intelligent Cost-Effective Electric Vehicle Charging Solutio...

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17th International Conference on COMmunication Systems and NETworkS, COMSNETS 2025

作者： Lohia, Akshat Pochhi, Aadesh Mishra, Shivansh Chakraborty, Nilotpal Indian Institute of Information Technology Department of Computer Science and Engineering Assam Guwahati India

ISBN: (纸本)9798331531195

In this work, we address the strategic placement and optimal sizing of electric vehicle charging stations for cities as well as highway traffic to minimize overall cost. We formulate the problem as a Mixed Integer Linear Programming (MILP) model and propose a set of greedy algorithms to obtain scalable solutions quickly. For city areas, we propose a systematic approach considering factors like population density and traffic patterns to maximize customer satisfaction and minimize costs. For highways and expressways, we account for deterministic inter-arrival times and maximum waiting times. Additionally, we propose an integrated approach that, when run for two cities and a connecting highway, demonstrates a total cost reduction compared to using separate algorithms for cities and highways. © 2025 IEEE.

关键词： Integer linear programming

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Urdu Sentiment Analysis Using Deep Learning 18

Urdu Sentiment Analysis Using Deep Learning

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18th International Conference on Open Source Systems and Technologies, ICOSST 2024

作者： Nasir, Imtisall Bazai, Sibghat Ullah Ghafoor, Muhammad Imran Marjan, Shah Balochistan University of Information Technology Engineering and Management Science Department of Computer Science Quetta Pakistan Balochistan University of Information Technology Engineering and Management Science Department of Computer Engineering Quetta Pakistan Pakistan Television Corporation Department of Engineering Lahore Pakistan Balochistan University of Information Technology Engineering and Management Science Department of Software Engineering Quetta Pakistan

ISBN: (纸本)9798331508692

Sentiment Analysis is a deep text analysis to determine the expressions from the written text, The Urdu language holds significant importance due to the usage of these social media platforms and content generated at these platform in Urdu. Effective sentiment analysis in Urdu can provide valuable information about social trends, customer feedback, and public opinion on different sets of topics in the Urdu context. This study uses an deep learning (ANN) Artificial Neural Network to predict and classify the sentiments in the Urdu language, labeling them into positive, negative, and neutral sentiments. To achieve the research goals, advanced preprocessing techniques were utilized such as removing stop words, emojis, emails, and characters, removing symbols, performing lemmatization to expand the meaning of text, tokenized each word by using Lexicon-based approach and ngram. The dataset are tweets dependent and have 1,140,021 tweets. These steps ensure that data is standardized and cleaned to be split into training and testing. The datasets were converted into sequence and categorized to be input into the ANN model. The ANN model was trained and achieved an accuracy of 90%, precision of 98%, and f1-score of 97%. These high values indicate that the model accurately predicted the sentiments in the Urdu text. However, during experimentation limitations were discovered due to the complexities of Urdu text, difficult script patterns, and linguistic problems that were not solved by the preprocessing technique. During analysis, the limitation was discovered due to the limited number of preprocessing techniques and tools for Urdu text. However, the study highlights the potential of neural networks, especially ANN in Urdu sentiment analysis. This study contributed significantly to Urdu natural language processing by providing robust techniques and identifying limitations to work on it as a research problem. © 2024 IEEE.

关键词： Natural language processing systems

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