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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Oberoi, Vansh Gautam Karthik, V. Dheeba, J. Raja Singh, R. Vellore Institute of Technology School of Computer Science and Engineering Tamil Nadu Vellore India Vellore Institute of Technology School of Electrical Engineering Tamil Nadu Vellore India Automotive Research Center Vellore Institute of Technology School of Electrical Engineering Tamil Nadu Vellore India

ISBN: (纸本)9798350370249

Industrial drive systems play a critical role in modern manufacturing processes, and their reliable operation is essential for productivity. These systems often consist of multiple interconnected control units that communicate through transmission control protocols. The security of these protocols remains vulnerable to attacks if the network is compromised. In this article, the security challenges faced by industrial electrical drive system is investigated and the strategies to enhance their resilience against cyber threats are proposed. The intended drive system is realized with a 2.2 kW squirrel cage induction motor and two-level voltage source inverter controlled using the F28379D microcontroller. The microcontroller is interfaced with the computer through USB for deploying the control program, as well as to monitor and tune the drive parameters. The focus lies on the computer, which provides speed commands to the microcontroller. By implementing these strategies, an effective intrusion detection system is tailored to the unique requirements of industrial electrical drive systems, safeguarding them against service disruption attacks. © 2024 IEEE.

关键词： Microcontrollers

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Automatic extraction of lightweight and efficient neural network architecture of heavy convolutional architectures to predict microsatellite instability from hematoxylin and eosin histology in gastric cancer

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Neural Computing and Applications 2024年第25期36卷 15295-15321页

作者： Rostami, Habib Ashkpour, Maryam Behzadi-Khormouji, Hamed Mokhtari, Maral Khayati, Armin Keshavarz, Ahmad Talatian Azad, Saeed Tabesh, Yahya Computer Engineering Department Faculty of Intelligent Systems Engineering and Data Science Persian Gulf University Bushehr75168 Iran Artificial Intelligence and Intelligent Healthcare Lab Artificial Intelligence and Data Mining Research Group Faculty of Intelligent Systems Engineering and Data Science ICT Research Institute Persian Gulf University Bushehr75168 Iran Faculty of New Science and Technologies University of Tehran Tehran Iran Department of Computer Science Imec-IDLab University of Antwerp Sint-Pietersvliet 7 Antwerp2000 Belgium Shahid Faghihi Hospital Shiraz University of Medical Sciences Shiraz Iran Department of Computer Science Engineering and IT School of Electrical and Computer Engineering Shiraz University Shiraz Iran Electrical Engineering Department Faculty of Intelligent Systems Engineering and Data Science Persian Gulf University Bushehr75168 Iran IoT and Signal Processing Research Group Faculty of Intelligent Systems Engineering and Data Science ICT Research Institute Persian Gulf University Bushehr75168 Iran Department of Mathematical Sciences Sharif University of Technology Tehran Iran

Cancers have emerged as a significant concern due to their impact on public health and society. The examination and interpretation of tissue sections stained with Hematoxylin and Eosin (H&E) play a crucial role in disease assessment, particularly in cases like gastric cancer. Microsatellite instability (MSI) is suggested to contribute to the carcinogenesis of specific gastrointestinal tumors. However, due to the nonspecific morphology observed in H&E-stained tissue sections, MSI determination often requires costly evaluations through various molecular studies and immunohistochemistry methods in specialized molecular pathology laboratories. Despite the high cost, international guidelines recommend MSI testing for gastrointestinal cancers. Thus, there is a pressing need for a new diagnostic modality with lower costs and widespread applicability for MSI detection. This study aims to detect MSI directly from H&E histology slides in gastric cancer, providing a cost-effective alternative. The performance of well-known deep convolutional neural networks (DCNNs) and a proposed architecture are compared. Medical image datasets are typically smaller than benchmark datasets like ImageNet, necessitating the use of off-the-shelf DCNN architectures developed for large datasets through techniques such as transfer learning. Designing an architecture proportional to a custom dataset can be tedious and may not yield desirable results. In this work, we propose an automatic method to extract a lightweight and efficient architecture from a given heavy architecture (e.g., well-known off-the-shelf DCNNs) proportional to a specific dataset. To predict MSI instability, we extracted the MicroNet architecture from the Xception network using the proposed method and compared its performance with other well-known architectures. The models were trained using tiles extracted from whole-slide images, and two evaluation strategies, tile-based and whole-slide image (WSI)-based, were employed and comp

关键词： Network architecture

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Predicting Future Automobile Ownership Trends in Uzbekistan Using Linear Regression 2nd

Predicting Future Automobile Ownership Trends in Uzbekistan ...

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2nd International Conference on Cyber Intelligence and Information Retrieval, CIIR 2023

作者： Ather, Danish Singh, Malikhan Arora, Kavita Pathak, Sonal Qurashi, Suhail Javed Singh, Anupam Amity University Tashkent Uzbekistan Computer Science and Engineering College of Engineering & Technology Mathura India School of Computer Applications Manav Rachna International Institute of Research and Studies Haryana Faridabad India Department of Computer Science and Engineering Graphic Era Hill University Dehradun India

ISBN: (纸本)9789819735938

The rapid urbanization and economic growth of Uzbekistan over the past decades have engendered significant changes in automobile ownership trends. Understanding these trends is crucial for infrastructure planning, sustainable transportation strategies, and environmental considerations. This Paper seeks to employ Machine Learning (ML) techniques to forecast the number of cars per 100 households in Uzbekistan by 2025, based on historical data from 2010, 2015, and 2020. The dataset, derived from official statistics provided by the State Committee of the Republic of Uzbekistan on Statistics, shows a remarkable increase from 21 cars per 100 households in 2010 to 48 by 2020. Leveraging regression models, we aim to predict the expected growth for 2025. Preliminary analyses hint at a continuing upward trend, impacted potentially by factors such as Population growth, GDP growth, urban migration, and increasing global connectivity. The findings from this research will not only serve as a prediction tool but will also shed light on the socio-economic factors influencing car ownership, providing valuable insights to policymakers and urban planners. While the limited data points present a challenge for complex model utilization, the study underscores the potential of ML in forecasting and the need for more comprehensive data collection in future studies. The predictive insights drawn from this research can significantly aid in shaping transportation policies, mitigating potential environmental impacts, and planning urban infrastructures in Uzbekistan. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Forecasting

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Modeling and Simulation of Unmanned Swarm System Based on Dynamic Structure DEVS 5

Modeling and Simulation of Unmanned Swarm System Based on Dy...

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2023 5th International Conference on Modeling, Simulation, Optimization and Algorithm, ICMSOA 2023

作者： Zhang, Wangyang Li, Qingwei Xu, Xin Li, Weiqing School of Computer Science and Engineering Nanjing University of Science and Technology Jiangsu Nanjing China The 28th Research Institute of China Electronics Technology Group Corporation Jiangsu Nanjing China

As a complex system with collaborative cooperation and capability emergence, the unmanned swarm system plays an important role in military, agriculture, aerospace and other fields. With the increasing complexity of unmanned swarm systems, traditional modeling methods cannot effectively describe the dynamics of swarm systems. In order to effectively design, use and evaluate unmanned swarm systems, this article conducts modeling and simulation of unmanned swarm systems based on DSDEVS(Dynamic Structure Discrete Event System Specification). Describe the unmanned swarm system from both behavioral and structural aspects. Based on component modeling, the microscopic behaviors of the unmanned platforms that constitute the unmanned swarm are described, and coupling relationships are established to describe the collaborative structure between unmanned platforms. According to the state transfer and event interaction characteristics of the unmanned swarm system, a synchronization mechanism is added to support changes in the swarm structure. This enables the unmanned swarm system to make adaptive structural adjustments according to environmental changes and its own goals during simulation operation, so as to achieve a state of enhanced capabilities and structural stability to complete the task. Finally, the effectiveness and feasibility of this method were verified through a simulation experiment of joint reconnaissance by a UAV swarm. © Published under licence by IOP Publishing Ltd.

关键词： Specifications

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Efficient Mobility in Next Generation Heterogeneous Wireless Networks Based on Enhanced Vertical Handoff Approach

International Journal of Interactive Mobile Technologies

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International Journal of Interactive Mobile Technologies 2025年第6期19卷 128-139页

作者： Kaliappan, Vishnu Kumar Padmapriya, T. Manikanthan, S.V. Ilavarasi, S. Suresh Kumar, B. Department of Computer Science and Engineering KPR Institute of Engineering and Technology Arasur Tamil Nadu Coimbatore India Melange Publications Puducherry India Melange Academic Research Associates Puducherry India Tamil Nadu Tiruchengode India Department of Electronics and Communication Engineering The Kavery Engineering College Mecheri Tamil Nadu Salem India

The progression of mobile technologies from first generation (1G) to fifth generation (5G) networks has brought about noteworthy advancement. The present network environment is characterized by the coexistence of various mobile networks and technologies, leading to overlapping or non-overlapping coverage areas, creating a next-generation heterogeneous landscape. Handoffs within the same network (i.e., GSM to GSM or vice versa) are known as horizontal or homogeneous handoffs. Conversely, when handoffs entail a transition to a different network type, such as moving from GSM to CDMA or vice versa, they are categorized as vertical or heterogeneous handoffs. Multiple link triggers, such as link down (LD), link going down (LGD), link detected, and link up, are employed to initiate and execute vertical handoff (VHO) when needed. We have determined the thresholds for vehicular speed and link triggers through extensive simulations, explicitly focusing on high-demand traffic types like video. This paper proposed an enhanced VHO approach based on IEEE 802.21, commonly called media independent handover (MIH), to improve Quality of Service (QoS) parameters, specifically mitigating packet loss (PL), and handoff delay and enhancing the user experience. © 2025 by the authors of this article.

关键词： 5G mobile communication systems

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SWPFOPLD: A Profiling and Optimizing Loader for SW26010Pro Processors 8

SWPFOPLD: A Profiling and Optimizing Loader for SW26010Pro P...

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8th International Conference on computer and Communication Systems, ICCCS 2023

作者： Qian, Hong Wu, Wei Zhu, Qi Wang, Fei Zhao, Jinwei Zheng, Yan School of Computer Science and Technology University of Science and Technology of China Hefei China Jiangnan Institute of Computing Technology System Software Research Department Wuxi China Tsinghua University Department of Computer Science and Technology Beijing China National Research Center of Parallel Computer Engineering and Technology Beijing China

ISBN: (纸本)9781665456128

The Sunway family supercomputers have achieved a series of remarkable achievements. However, the toolchains provided by them are not perfect, which has brought great challenges to the development of high-performance application software. In this paper, a profiling and optimizing tool is proposed to assist people to analyze and optimize the performance of their programs. SWPFOPLD is independent of the application program and gathers the runtime performance data through the PMUs, an automatically hot functions rearrange optimization based on the performance data is furtherly accomplished. The evaluation shows that SWPFOPLD can be easily and effectively used to analyze and optimize the performance of the application programs. © 2023 IEEE.

关键词： Supercomputers

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Earlier Prognosis of Malignant Tumor in Cerebral Magnetic Resonance Images using Machine Learning 7

Earlier Prognosis of Malignant Tumor in Cerebral Magnetic Re...

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7th International Conference on Inventive Computation Technologies, ICICT 2024

作者： Mahesh Prasanna, K. Lohumi, Yogesh Renukalatha, S. Patil, Harshal Maranan, Ramya Vekariya, Vipul Vivekananda College of Engineering And Technology Department of Electronics And Communication Engineering Karnataka Puttur574203 India Graphic Era Deemed To Be University Department of Computer Science & Engineering Uttarakhand Dehradun248002 India Sri Siddhartha Institute of Technology Department of Computer Science And Engineering Karnataka Tumkur572 105 India Computer Science And Engineering Symbiosis Institute of Technology Maharashtra Pune412 115 India SIMATS Saveetha School of Engineering Department of Research And Innovation Tamil Nadu Chennai602105 India Parul University Parul Institute of Engineering And Technology Department of Computer Science And Engineering Post Limda Gujarat Waghodia391760 India

ISBN: (纸本)9798350359299

Adults are at risk of developing brain tumors, which are characterized by the rapid multiplication of abnormal brain cells and can cause considerable damage or even loss of organ function. These tumors can vary widely in dimensions, texture, and position. Magnetic resonance imaging (MRI) is critical in the detection of malignant tumors. However, detecting brain tumors manually is tedious, error-prone, and time-consuming. The proposed solution includes optimized feature selection and Machine Learning (ML) models for enhancing the accuracy of brain tumor detection. This method can identify meningioma, glioma, and pituitary gland malignancies with high accuracy. First, the Brain MRIs are collected and pre-processed to increase their visual quality. Second, the Gray Level Co-Occurrence Matrix (GLCM) is used to extract the features from images. Third, Genetic Algorithm (GA) and Fire-Fly Optimization (FFO) are used to optimize the created feature vectors, allowing for the selection of more appropriate features. Fourth, for classification, three ML models such as Support Vector Machine (SVM), Naïve Bayes (NB), and Random Forest (RF) are employed. Finally, evaluation is done to determine the best combination of feature selection technique and ML. SVM using FFO features exceeds all other approaches, with an accuracy score of 98.55%, sensitivity of 97.81%, specificity of 99.26%, precision of 97.95%, and F1 of 98.6%. The SVM with FFO features can help the physician in the early diagnosis of brain tumors. © 2024 IEEE.

关键词： Magnetic resonance imaging

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HUSS:A Heuristic Method for Understanding the Semantic Structure of Spreadsheets

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Data Intelligence 2023年第3期5卷 537-559页

作者： Xindong Wu Hao Chen Chenyang Bu Shengwei Ji Zan Zhang Victor S.Sheng Key Laboratory of Knowledge Engineering with Big Data(the Ministry of Education of China) Hefei University of TechnologyChinaSchool of Computer Science and Information EngineeringHefei University of TechnologyHefeiChina Research Institute of Artificial Intelligence Zhejiang LabHangzhouChina Department of Computer Science Texas Tech UniversityLubbockTX 79409USA

Spreadsheets contain a lot of valuable data and have many practical *** key technology of these practical applications is how to make machines understand the semantic structure of spreadsheets,e.g.,identifying cell function types and discovering relationships between cell *** existing methods for understanding the semantic structure of spreadsheets do not make use of the semantic information of cells.A few studies do,but they ignore the layout structure information of spreadsheets,which affects the performance of cell function classification and the discovery of different relationship types of cell *** this paper,we propose a Heuristic algorithm for Understanding the Semantic Structure of spreadsheets(HUSS).Specifically,for improving the cell function classification,we propose an error correction mechanism(ECM)based on an existing cell function classification model[11]and the layout features of *** improving the table structure analysis,we propose five types of heuristic rules to extract four different types of cell pairs,based on the cell style and spatial location *** experimental results on five real-world datasets demonstrate that HUSS can effectively understand the semantic structure of spreadsheets and outperforms corresponding baselines.

关键词： Spreadsheet semantic structure Information extraction Heuristics Cell function analysis Table structure analysis

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Real-Time Spammers Detection Based on Metadata Features with Machine Learning

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Intelligent Automation & Soft Computing 2023年第12期38卷 241-258页

作者： Adnan Ali Jinlong Li Huanhuan Chen Uzair Aslam Bhatti Asad Khan School of Computer Science and Technology University of Science and Technology of ChinaHefei230026China School of Information and Communication Engineering Hainan UniversityHaikou570228China Metaverse Research Institute School of Computer Science and Cyber EngineeringGuangzhou UniversityGuangzhou510006China

Spammer detection is to identify and block malicious activities performing *** users should be identified and terminated from social media to keep the social media process organic and to maintain the integrity of online social *** research aimed to find spammers based on hybrid approaches of graph mining,posted content,and metadata,using small and manually labeled ***,such hybrid approaches are unscalable,not robust,particular dataset dependent,and require numerous parameters,complex graphs,and natural language processing(NLP)resources to make decisions,which makes spammer detection impractical for real-time *** example,graph mining requires neighbors’information,posted content-based approaches require multiple tweets from user profiles,then NLP resources to make decisions that are not applicable in a real-time *** fill the gap,firstly,we propose a REal-time Metadata based Spammer detection(REMS)model based on only metadata features to identify spammers,which takes the least number of parameters and provides adequate *** is a scalable and robust model that uses only 19 metadata features of Twitter users to induce 73.81%F1-Score classification accuracy using a balanced training dataset(50%spam and 50%genuine users).The 19 features are 8 original and 11 derived features from the original features of Twitter users,identified with extensive experiments and ***,we present the largest and most diverse dataset of published research,comprising 211 K spam users and 1 million genuine *** diversity of the dataset can be measured as it comprises users who posted 2.1 million Tweets on seven topics(100 hashtags)from 6 different geographical *** REMS’s superior classification performance with multiple machine and deep learning methods indicates that only metadata features have the potential to identify spammers rather than focusing on volatile posted content and complex graph ***

关键词： Spam detection online social networks metadata machine learning

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Equalizer Zero-Determinant Strategy in Discounted Repeated Stackelberg Asymmetric Game

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Journal of Systems science & Complexity 2024年第1期37卷 184-203页

作者： CHENG Zhaoyang CHEN Guanpu HONG Yiguang Key Laboratory of Systems and Control Academy of Mathematics and Systems ScienceChinese Academy of SciencesBeijing 100190China School of Mathematical Sciences University of Chinese Academy of SciencesBeijing 100049China School of Electrical Engineering and Computer Science KTH Royal Institute of Technology10044 StockholmSweden. Department of Control Science and Engineering Tongji UniversityShanghai 201804China Shanghai Research Institute for Intelligent Autonomous Systems Tongji UniversityShanghai 210201China

This paper focuses on the performance of equalizer zero-determinant(ZD)strategies in discounted repeated Stackelberg asymmetric *** the leader-follower adversarial scenario,the strong Stackelberg equilibrium(SSE)deriving from the opponents’best response(BR),is technically the optimal strategy for the ***,computing an SSE strategy may be difficult since it needs to solve a mixed-integer program and has exponential complexity in the number of *** this end,the authors propose an equalizer ZD strategy,which can unilaterally restrict the opponent’s expected *** authors first study the existence of an equalizer ZD strategy with one-to-one situations,and analyze an upper bound of its performance with the baseline SSE *** the authors turn to multi-player models,where there exists one player adopting an equalizer ZD *** authors give bounds of the weighted sum of opponents’s utilities,and compare it with the SSE ***,the authors give simulations on unmanned aerial vehicles(UAVs)and the moving target defense(MTD)to verify the effectiveness of the proposed approach.

关键词： Discounted repeated Stackelberg asymmetric game equalizer zero-determinant strategy strong Stackelberg equilibrium strategy

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