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Quality Evaluation of Industry Education Integration based on Sculptor Optimization Algorithm with Fuzzy C-Means 1

Quality Evaluation of Industry Education Integration based o...

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1st international conference on Intelligent systems and Computational Networks, ICISCN 2025

作者： Chen, Meifeng Chen, Li Liu, Yanmin Deng, Dejun Li, Zhiqin Xiamen University of Technology School of Architectural Science and Civil Engineering Xiamen China

ISBN: (纸本)9798331529246

In recent years, industry-education integration goal to bridge the gap among academic learning and industry demands by developing collaboration between educational institutions and industries. The existed methods K-Means Clustering (KMC) faced several challenges such as insufficient collaboration mechanisms, data inconsistency, and difficulties in measuring the outcomes. To overcome these issues, this research proposes Sculptor Optimization Algorithm with Fuzzy C-Means (SOA-FCM) based evaluation framework. Initially, data was collected from Z School and then, the development of measurement scales is done through Service Quality (SERVQUAL) scale. The SERVQUAL scale consists of five dimensions including course quality, self-efficacy, perceived interest, perceived usefulness. Then, SOA was employed for optimization to refine the collected data by dynamically adapt parameters and optimize complex multi-criteria problems. Finally, the FCM is introduced to evaluate the course quality and student interests in course which helps to support data-driven decisions for improving the course quality. From the results, the proposed SOA-FCM model attained better outcomes compared to existing Support Vector Machine in terms of accuracy, precision, recall and F1 Score by achieving 99.02%, 97.31%, 96.76% and 98.32% respectively. © 2025 ieee.

关键词： K-means clustering

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Securing Wireless Communication: A Comprehensive Arduino-based Approach 3

Securing Wireless Communication: A Comprehensive Arduino-bas...

引用

3rd international conference on Intelligent data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Navaneeth, M. Vamsi Krishna, K. Belwal, Meena Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (纸本)9798331527549

This research aims at developing Arduino solutions to improve the security of wireless communication systems for this work and to present a broad intervention plan for achieving this aim. Incorporating the features of Arduino microcontrollers the system has the potential of mitigating some of the weak wireless communications paradigms. The methodology consists in applying the use of such measures as the encryption algorithms and authentication and the use of secure data transmission protocols. The approach, proposed, not only enhances the security of wireless communication but also is cost effective and easy to install. In this respect, it is hoped that the outcomes of the project will offer effective and efficient solutions for implementing quality security elements for wireless transmission with practical and experimental *** purpose of this work is to generate and transmit the text message through a wireless communication system in a secured way. This facility prevails in the mobile network for which the user pays for it, whereas the system designed here can be used as personal communication system and information can be transmitted at free of *** result of the work is expected to be a secure information transmission with wireless technology implemented and encrypt the data that need to be transmitted without letting interception of a third party and reach the destination, convert and decrypt the message into readable *** work more particularly targets at reducing some of the risks associated with present wireless communication systems through use of effective encryption and authentication. The results are expected to include a major decrease in the interception risks, as well as the effective and safe text message transmission with real-world evidence as proof. © 2025 ieee.

关键词： Microcontrollers

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Multi-Quadrotor Cooperative Encirclement and Capture Approach in Obstacle Environments

Multi-Quadrotor Cooperative Encirclement and Capture Approac...

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2025 ieee international conference on Mechatronics, ICM 2025

作者： Li, Zhenyu Wu, Ruixin Wang, Jin Hu, Chuanqi Zheng, Zhi Meng, Jun Lu, Guodong Zhejiang University State Key Laboratory of Fluid Power and Mechatronic Systems Hangzhou310058 China Zhejiang University Zhejiang Key Laboratory of Industrial Big Data and Robot Intelligent Systems Hangzhou310058 China Robotics Research Center of Yuyao City Ningbo315400 China College of Electrical Engineering Zhejiang University Hangzhou310027 China Center for Data Mining and Systems Biology College of Electrical Engineering Zhejiang University Hangzhou310027 China

ISBN: (纸本)9798331533892

This article proposes an algorithm designed for autonomous quadrotors to capture target quadrotors in complex obstacle environments, enabling multiple pursuers to perform distributed capture while simultaneously avoiding obstacles and collisions. We first introduce a 3D obstacle space representation method based on safety-priority reachable region (SPRR) and implement real-time construction for general point cloud maps. Subsequently, leveraging SPRR, we employ an area minimization approach to ensure the effective capture of the evader within a limited time. The outcomes are then integrated into a MINCO-based trajectory planning framework to ensure their practical execution by real quadrotors. Relying on SPRR, the optimization problem is simplified and the solution efficiency is improved. In the scenario of simulating the dynamics of real quadrotors, we compare the proposed algorithm with state-of-the-art evasion and pursuit methods, validating its effectiveness and advancements. This work presents a practical solution for multi-quadrotor collaborative capture. © 2025 ieee.

关键词： Motion planning

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Artificial Intelligence to Enhance the Security of Internet of Things: A Comprehensive Review 3

Artificial Intelligence to Enhance the Security of Internet ...

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3rd international conference on Intelligent data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Dheekshaa, V.M. Harsha, V. Sindhu, M. Pavithra, S. Dept. of Computer Science and Engineering Chennai Institute of Technology Tamil Nadu Chennai600069 India

ISBN: (纸本)9798331527549

The Internet of Things is the term we call when devices are connected to the network and work together to provide a better experience for users, facilitate better decision-making, and improve operations. As with any invention, this one also has its disadvantages. IoT devices have complex structures and low processing capacities. There are serious security risks in designing IoT like poor authentication, data leakage, and DDoS attacks. In this review paper, how AI can secure IoT is discussed. A few AI-driven strategies that help mask all these weaknesses are also discussed regarding intrusion detection systems (IDS), behavioural analytics, anomaly detection, and real-time threat monitoring. It presents a four-layered IoT architecture consisting of perception, network, support, and application layers. The use of the IoT systems will become more flexible and efficient in data security and collection, secure and encrypted communication, and predictive security analytics. AI will play the same role in improving the threat perception, real-time identification, and response, as this will make it indispensable to ensure the IoT environment security. This work emphasizes the urgent need for solid AI-powered, multilayered adaptive security frameworks to match the evolving demands of cybersecurity. © 2025 ieee.

关键词： Network intrusion

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Enhancing Naval Mine Detection through Augmented Side Scan Sonar data and Synthetic Image Generation for Manned and Unmanned Teaming systems

Enhancing Naval Mine Detection through Augmented Side Scan S...

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2025 international conference on Electronics, Information, and Communication, ICEIC 2025

作者： Lim, Jeongkyu Park, Chanyeong Paik, Joonki National Defence AI school Chung-Ang University Republic of Korea Navy Seoul Korea Republic of Chung-Ang University Department of Image Seoul Korea Republic of National Defence AI school Chung-Ang University Department of Image Seoul Korea Republic of

ISBN: (纸本)9798331510756

This paper introduces a novel method for augmenting Side Scan Sonar (SSS) data, which is essential for naval mine search operations, to address the current shortage of mine-related defense datasets. SSS is a specialized imaging technology used to capture detailed underwater images, typically of the seabed, for detecting objects like mines. To enhance the robustness of mine classification tasks, we utilize overseas SSS datasets and augment them with data from coastal seabed environments through the image mixup technique. Image mixup is a data augmentation method that blends multiple images to create new synthetic samples. A series of experiments were conducted using the YOLOv8 real-time object detection model, which is well-suited for high-speed and accurate object identification. Our results show that the inclusion of synthetic data significantly improved mine detection performance. This approach provides a promising solution to the existing data limitations, particularly in the context of Manned and Unmanned Teaming (MUM-T) systems, where human operators and autonomous systems collaborate. MUM-T is crucial for expanding the Navy's capabilities in mine detection operations, and our findings suggest a viable strategy for enhancing overall mission effectiveness. © 2025 ieee.

关键词： Sonar

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Accelerating Quantum Circuit Simulation with Symbolic Execution and Loop Summarization 24

Accelerating Quantum Circuit Simulation with Symbolic Execut...

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43rd international conference on Computer-Aided Design, ICCAD 2024

作者： Chen, Tian-Fu Chen, Yu-Fang Jiang, Jie-Hong Roland Jobranová, Sára Lengál, Ondrej Grad. School of Advanced Technology National Taiwan University Taipei Taiwan Institute of Information Science Academia Sinica Taipei Taiwan Grad. Inst. of Electronics Engineering National Taiwan University Taipei Taiwan Faculty of Information Technology Brno University of Technology Brno Czech Republic

ISBN: (纸本)9798400710773

Quantum circuit simulation is the basic tool for reasoning over quantum programs. Despite the tremendous advance in the simulator technology in the recent years, the performance of simulators is still unsatisfactory on non-trivial circuits, which slows down the development of new quantum systems. In this work, we develop a loop summarizing simulator based on multi-terminal binary decision diagrams (MTBDDs) with efficiently customized quantum gate operations. The simulator is capable of automatic loop summarization using symbolic execution, which saves repetitive computation for circuits with iterative structures. Experimental results show the simulator outperforms state-of-the-art simulators on some standard circuits, such as Grover’s algorithm, by several orders of magnitude. © 2024 Copyright is held by the owner/author(s).

关键词： Quantum electronics

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Deep learning based Image Compression, Encryption, and Recovery using Chaotic map and Compressive sensing 5

Deep learning based Image Compression, Encryption, and Recov...

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5th international conference on Emerging systems and Intelligent Computing, ESIC 2025

作者： Tarun, R.G. Aggarwal, Priyanshu Ashwini, K. Amrita Vishwa Vidhyapeetham Amrita School of Computing Department of Computer Science and Engineering Chennai India Amrita School of Computing Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798331522100

Images are present in everything these days, from satellite photography to medical scans, and they have a profound impact on how people view and understand the world. The daily generation of an ever-increasing volume of image data has made it imperative to identify effective ways to convey these images. This research presents a novel picture recovery approach that makes use of Chaotic Maps for increased security and Compressive Sensing for effective compression. Compressive Sensing makes use of the sparsity seen in images to minimize the number of data points required. Chaotic Map is used in encryption, is also to guarantee the security of the compressed data. The Neural network model built to recover images is a hybrid of Conditional Generative Adversarial Networks (C-GANs), patch discriminator and U-net generator. This allows for high-quality image reconstruction. This approach is suitable for usage in healthcare and marine life. © 2025 ieee.

关键词： Image compression

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Target Classification in Radar Using Deep Learning 1

Target Classification in Radar Using Deep Learning

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1st ieee international conference on Smart and Sustainable Developments in Electrical Engineering, SSDEE 2025

作者： Sahu, Suchitra Abhiram, Perumulla Menghal, P.M. Birla Institute of Technology and Science Pilani - Hyderabad Campus Department of Electrical and Electronics Engineering Hyderabad India Military College of Electronics and Mechanical Engineering Secunderabad Faculty of Electronics Telangana Hyderabad India

ISBN: (纸本)9798331542108

In radar systems, precise target classification is essential for effective situational awareness. We developed a Convolutional Neural Network (CNN)-based approach to classify radar targets using their signal characteristics automatically. Compared to traditional methods, our solution demonstrates higher accuracy, robustness, and computational efficiency while addressing challenges like data labelling, interpretability, and signal variability. The results highlight the potential of deep learning to transform radar systems, enhancing applications in military surveillance and autonomous navigation. This work marks a significant step toward seamlessly integrating deep learning with radar technologies. © 2025 ieee.

关键词： Surveillance radar

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Enhancing Peer-to-Peer Network Security using 3D Convolutional Autoencoder based Anomaly Detection 1

Enhancing Peer-to-Peer Network Security using 3D Convolution...

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1st international conference on Intelligent systems and Computational Networks, ICISCN 2025

作者： Rana Veer Samara Sihman Bharattej, R. Ajznblasm, Zaid Satish, E.G. Adlin Sylvia, A. Devi, M. Saint Leo University Department of Computer Science FL United States The Islamic University of Al Diwaniyah College of Technical Engineering Department of Computers Techniques Engineering Al Diwaniyah Iraq Nitte Meenakshi Institute of Technology Department of Computer Science and Engineering Bengaluru India Dhanalakshmi Srinivasan College of Engineering and Technology Department of Computer Science and Engineering Mamallapuram India New Prince Shri Bhavani College of Engineering and Technology Department of Electrical and Electronics Engineering Chennai India

ISBN: (纸本)9798331529246

In this era, Peer-to-Peer (P2P) networks have developed as a revolutionized network for data distribution and alternate to traditional client-server network. The features of P2P networks allow a distributed group of users to stake resources in the form of digital content or information. However, P2P networks lack in controlling access and it has limited security. In this research, Three Dimensional-Convolutional Autoencoder (3D-CA) is proposed to improve the security in P2P network. Initially, the data is obtained from University of New South Wales Network Behavior 2015 (UNSW-NB15) dataset and then preprocessed with data cleaning to ensure data quality. After that, the P2P autoencoder architecture is employed to learn the normal patterns which employs the detection in P2P network. Finally, the proposed 3D-CA is trained to detect the anomalies based on reconstruction error between input node and reconstructed pattern. From the results, the proposed 3D-CA model attained better outcomes compared to existing K-means algorithm in terms of accuracy, precision as well as F1-score by achieving 0.764, 0.90 and 0.80 respectively. © 2025 ieee.

关键词： Peer to peer networks

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E-Waste Management using Deep Learning 6

E-Waste Management using Deep Learning

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6th international conference on Mobile Computing and Sustainable Informatics, ICMCSI 2025

作者： Kumar, Boddu Manoj Sujitha, Kaduru Abinaya, K. School of Computer Science Engineering with Blockchain Technology Sathyabama Institute of Science and Technology Tamil Nadu Chennai600119 India School of Computer Science Engineering with Internet of Things Sathyabama Institute of Science and Technology Tamil Nadu Chennai600119 India School of Computer Science and Engineering Sathyabama Institute of Science and Technology Tamil Nadu Chennai600119 India

ISBN: (纸本)9798331522667

Using the YOLO v8 model and deep learning approaches, this study explores the field of e-waste management and provides effective item detection. Our research attempts to increase the accuracy and scalability of e-waste detection, simplifying its incorporation into current waste management systems. Through utilizing deep learning, our goal is to transform the way that electronic waste is categorized, from tiny parts to larger appliances, allowing for more efficient as well as environmentally friendly management techniques. When it comes to aiming for higher performance, privacy comes first. Methods of preservation guarantee the accuracy of sensitive data correlated with pictures of e-waste. By means of execution utilizing data anonymization and encryption methods, we ensure compliance with legal requirements while safeguarding against potential privacy threats and ethical issues. The YOLO v8 model achieved an average precision (Map) of 97.1% in detecting e-waste items, demonstrating its effectiveness and scalability for real-world applications. © 2025 ieee.

关键词： Differential privacy

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