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22nd International Learning and Technology Conference, L and T 2025

作者： Mazhar Ul Haq, Qazi Yao, Leethar Lin, Bo-Yu Yuan Ze University Dept. of IBPI and Computer Science and Engineering Zhongli Taiwan National Taipei University of Technology Dept. of Electrical Engineering Taipei Taiwan

ISBN: (纸本)9798331532215

Person re-identification has been an important issue of surveillance systems in smart cities. However, this requires huge datasets to supervise deep learning models for accurately identifying and tracking people in smart cities. In this paper, we propose adversarial learning in integration with a mutual mean teaching model known as adversarial teaching to take the issue of domain adaptation. Initially, they learn a source dataset in a supervised way. This model information is then transferred to another model through unsupervised learning and pseudo-labels are generated by generative adversarial learning. The multi-class invariance is handled by adversarial learning. The performance of the proposed model is evaluated on two different datasets source and target datasets. Multiple experiments on Duke and Market datasets indicate higher accuracy of F-1 score on both datasets compared to previous methods, demonstrating the superior performance of the proposed method. © 2025 IEEE.

关键词： Adversarial machine learning

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Design and Implementation of Smart Classroom Using Cisco Packet Tracer 1

Design and Implementation of Smart Classroom Using Cisco Pac...

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1st IEEE International Conference on Advances in Electronics, Communication, Computing and Intelligent Information systems, ICAECIS 2023

作者： Duvvuri, Kavya Kanisettypalli, Harshitha Kunisetty, Jaswanth Khanna, Manju Amrita School of Engineering Dept. of Computer Science Engineering Bengaluru India

ISBN: (纸本)9798350348057

Schools and colleges play a very crucial role in everyone's life. It is very important to impart good morals and safe education to future generations. As days go by, the necessity for faster job completion and smarter devices is increasing. The safety of students becomes important as the number of students enrolling in schools is gradually increasing. Discipline and time management skills must be imparted to students from a tender age in the school environment. Both safety and discipline can be ensured by making use of devices that are smarter and remotely controllable. Through this work, we have introduced the design of a smart classroom using a network simulation called Cisco Packet Tracer. The work is based on the concept of the Internet of Things. It has multiple subsystems that together make the class a wonderful place for students to build their future. © 2023 IEEE.

关键词： Students

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Real-time Event Streaming for Financial Enterprise System with Kafka 3

Real-time Event Streaming for Financial Enterprise System wi...

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3rd IEEE Asian Conference on Innovation in Technology, ASIANCON 2023

作者： Sanjana, N. Raj, Sinchana Vishalakshi Prabhu, H. Sandhya, S. Rv College of Engineering Dept. of Computer Science & Engineering Bengaluru India

ISBN: (纸本)9798350302288

Real-time event streaming refers to the continuous and immediate processing and analysis of events as they occur in real time. Presently, Apache Kafka is a very popular and preferred framework for real-time data streaming. Data stream pipelines to serve enormous data for large organizations are built between systems in Kafka for real-time streaming. Kafka provides a distributed, scalable, and fault-tolerant platform for capturing, storing, and processing streams of events. It provisions seamless integration with different systems and applications, enabling real-time data analysis and processing. Thus, event streaming with Kafka is a modern approach to processing and analyzing large volumes of data in real-time. Event streaming with Kafka has a wide range of applications in varied industries such as finance, e-commerce, and more. It has the potential to transform the way businesses operate by enabling faster decision-making, improved operational efficiency, and better customer experiences. With financial enterprise application as our main goal, we focus on how Kafka could be a better-suited alternative to enhance event streaming. © 2023 IEEE.

关键词： Decision making

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File Encryption using Noise Images as Key 3

File Encryption using Noise Images as Key

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3rd International Conference on Smart Data Intelligence, ICSMDI 2023

作者： Bhagya Lakshmi, A. Pradeep, R. Seshadri, K.S. Shriraam, B. Easwari Engineering College Dept. of Computer Science & Engineering TN India

ISBN: (纸本)9781665464871

Everything in today's highly connected digital world is increasingly dependent on instantaneous global data transfers. Internet efficiency facilitates our daily lives. Sharing information online presents significant security dangers and difficulties in the modern day. The use of cryptography is the means through which sensitive information can be protected from various threats. Improved cryptosystems technology is a no-brainer for securing communication networks. An improved cryptosystem is the focus of our research, and to that end we have presented a novel method of data encryption and decryption that guarantees increased efficiency over current best practises. This research work has proposed a noise image encryption and decryption scheme, wherein the noise signal is randomly selected to set the initial values for a chaotic system which also enhances the security of the system. for understanding the effectiveness of the proposed system. Experimental results confirm that the proposed chaos-based cryptosystem is efficient and suitable for information (image) transmission in a highly secured way. This study has developed a file encryption system, where encrypted file is saved to decrypt and download later. The main difference between the existing and proposed system is the implementation of file encryption system using noise images that store key values inside it. © 2023 IEEE.

关键词： Cryptography

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Enhancing Spam Email Detection with a Soft Voting Ensemble of Optimized Machine Learning

Enhancing Spam Email Detection with a Soft Voting Ensemble o...

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2024 IEEE International Conference on Computing, Applications and systems, COMPAS 2024

作者： Tazwar, Asif Daiyan, Md Mahir Jiabul Hoque, Md Saifuddin, Mohammed Khaliluzzaman, Md. International Islamic University Chittagong Dept. of Computer and Communication Engineering Chittagong Bangladesh Fse International Islamic University Chittagong Dept. of Computer and Communication Engineering Chittagong Bangladesh Fse International Islamic University Chittagong Dept. of Computer Science and Engineering Chittagong Bangladesh

ISBN: (纸本)9798331529765

Spam email detection is crucial for cybersecurity, as it protects user privacy and reduces security risks. The persistent presence of spammers necessitates continuous improvements in spam filtering measures. To address this challenge, this study employs Grid Search Optimizer to fine-tune the parameters of four distinct classifiers: Support Vector Machine (SVM), Random Forest, Naive Bayes, and XGBoost. Soft Voting serves as the final classifier, enhancing overall accuracy by integrating results from optimized classifiers to detect and classify spam emails across the Spam Mails Dataset and Enron1 Dataset. The experimental study compared the proposed model with relevant works and found that hyperparameter tuning and Soft Voting significantly enhanced its performance compared to existing approaches. The proposed ensemble model outperformed individual classifiers, achieving accuracies of 99.32% and 99.12%, respectively. Also, the ensemble model demonstrated an AUC of 1.00 on both datasets, indicating its effectiveness in distinguishing between spam and ham emails. This approach surpasses prior studies in accuracy, generalization, and robustness by innovatively combining Grid Search and Soft Voting. The constructed model exhibits high effectiveness and efficiency in the detection of spam emails. © 2024 IEEE.

关键词： Decision trees

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Predicting the Likelihood of a Recurrent Febrile Seizure Using Thermographic Images and Deep Learning with Rules Generation 1

Predicting the Likelihood of a Recurrent Febrile Seizure Usi...

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1st International Conference on AIML-Applications for engineering and Technology, ICAET 2025

作者： Patil, Pratima Deshpande, Deepa S. MGM University Jawaharlal Nehru Engineering College Dept. of Computer Science and Engineering Chhatrapati Sambhajinagar431003 India

ISBN: (纸本)9798350355611

Recurrent febrile seizures are a common concern in children, often triggered by rapid changes in body temperature during fever episodes. For rapid action and care, early and accurate seizure recurrence prediction is crucial. Limited techniques were developed for predicting the likelihood of a Recurrent Febrile Seizure using a Thermographic image. Several drawbacks were considered for recurrent febrile seizure prediction such as lower performance, high time consumption, and error rate. Hence, the proposed technique is introduced to provide an efficient Deep Learning (DL)-based technique. Initially, the data is gathered from datasets, and the Intra-Attentional Convolutional ResNet (InAtt-CRNet) with the Yolov5-Face model detects the face and reduces the processing time by tacking the face with a bounding box. The patient's forehead or nose temperature is monitored using the Region of Interest (ROI) finding approach following face detection and tracking in each frame. Then, the temperature is predicted using the Enhanced Convolutional Capsule Network (EC2Net). The body temperature, age, duration of fever, and family history are used to construct the clinical decision rule. The Twin-directional Gated Long Short-term memory (TdG-LSTM) is used to predict the likelihood of a subsequent febrile seizure. The proposed technique achieves 98.16% accuracy, 98.45% precision, 0.12 RMSE, and 29 seconds as processing time. © 2025 IEEE.

关键词： Long short-term memory

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Characterization of ScAlN/GaN Toward Electronic Device Application

Characterization of ScAlN/GaN Toward Electronic Device Appli...

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2024 IEEE BiCMOS and Compound Semiconductor Integrated Circuits and Technology Symposium, BCICTS 2024

作者： Maeda, Takuya Wakamoto, Yusuke Kobayashi, Atsushi The University of Tokyo Dept. of Electrical Engineering and Information Systems Tokyo Japan Tokyo University of Science Dept. of Material Science and Technology Tokyo Japan

ISBN: (纸本)9798331541248

Scandium Aluminum Nitride (ScAIN) is an attractive material as a barrier layer of GaN HEMTs owing to its ultrawide bandgap, strong polarization and ferroelectric. In the paper, the recent progress in the characterization of ScAlN/GaN heterostructure prepared by low-temperature sputtering method is presented. Structural and optical properties of epitaxial pseudomorphic ScAIN with Sc compositions of 6,10 and 14% on GaN are investigated by AFM, XRD and spectroscopic ellipsometry. Consistent thicknesses of ∼20nm were obtained by XRD and spectroscopic ellipsometry. Dielectric function and optical bandgap are determined. © 2024 IEEE.

关键词： Gallium nitride

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Balancing Safety and Security in Autonomous Driving systems: A Machine Learning Approach with Safety-First Prioritization 40

Balancing Safety and Security in Autonomous Driving Systems:...

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40th Annual computer Security Applications Conference Workshops, ACSACW 2024

作者： Hasani, Afshin Nia, Mehran Alidoost Atani, Reza Ebrahimi University of Guilan Dept. of Computer Engineering Rasht Iran Shahid Beheshti University Faculty of Computer Science and Engineering Tehran Iran

ISBN: (纸本)9798331532819

In the era of autonomous vehicles (AVs), ensuring the safety and security of the system is an ongoing challenge, particularly when faced with increasingly cyber-Attacks such as GPS spoofing and man-in-The-middle. This paper presents a novel reinforcement learning (RL)-based framework that prioritizes safety over security concerns in AVs, ensuring that life-critical tasks such as collision avoidance and speed control are always maintained, even in the presence of security threats. Our system leverages the MAPE-K loop to dynamically adapt to changing conditions, using RL in the Analyzing phase to make real-Time decisions that maximize cumulative safety rewards while addressing security risks that directly affect operational integrity. We demonstrate how our RL-based self-Adaptive system mitigates security threats while maintaining safe driving behaviors through various simulations and experiments. The proposed system shows significant improvements in collision avoidance and threat detection, ultimately ensuring that the AV's safety is preserved in even the most challenging scenarios. © 2024 IEEE.

关键词： Adversarial machine learning

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An Edge AI and Adaptive Embedded System Design for Agricultural Robotics Applications 25

An Edge AI and Adaptive Embedded System Design for Agricultu...

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30th Asia and South Pacific Design Automation Conference, ASP-DAC 2025

作者： Huang, Chun-Hsian Chen, Zhi-Rui Hsu, Huai-Shu Dept. Electrical Engineering National Changhua University of Education Changhua Taiwan Dept. Computer Science and Information Engineering National Taitung University Taitung Taiwan

ISBN: (纸本)9798400706356

This work presents the AgrBot, an agricultural robot designed to intelligently estimate and predict crop pest and disease severity (PDS). The AgrBot incorporates two binarized neural network (BNN) hardware modules for recognizing target crops and estimating their PDS. In a resource-constrained FPGA-based design, these BNN hardware modules can be configured on-demand, showcasing system adaptivity. Furthermore, a multimodal model that integrates crop images, sensor data, and time features is presented for predicting PDS. Employing edge artificial intelligence (AI) through the BNN hardware modules and the multimodal model enables the AgrBot to determine if biological agents are applied to protect crops from pests and diseases, creating a comprehensive agricultural cyber-physical system (CPS). Experimental results demonstrate accuracies of 76.3% for recognizing target crops, 65.3% for estimating PDS, and 67% for predicting PDS. In comparison to existing microprocessor-based design methods, the AgrBot's BNN hardware modules improve frames per second (FPS) by a factor of 790, while the multimodal model reduces processing time by up to 50.9%. © 2025 Copyright is held by the owner/author(s). Publication rights licensed to ACM.

关键词： Digital storage

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Optimal Scheduling in Multiserver Queues

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Performance Evaluation Review 2024年第3期51卷 29-32页

作者： Grosof, Isaac Carnegie Mellon University Computer Science Department United States Northwestern University Dept. of Industrial Engineering & Management Sciences United States

Scheduling theory is a key tool for reducing latency (i.e. response time) in queueing systems. Scheduling, i.e. choosing the order in which to serve jobs, can reduce response time by an order of magnitude with no additional resources. Scheduling theory is well-developed in single-server systems, where one job is processed at a time. However, little is known about scheduling in multiserver systems, where many jobs are processed at once. Results are especially limited in stochastic multiserver scheduling theory. Today’s data-centers have thousands of servers, and scheduling theory is unable to analyze such systems. My thesis proves the first optimality results and first closed-form bounds on mean response time for scheduling policies in stochastic multiserver models which reflect the behavior of modern computing systems. My thesis proves novel optimality results in each of three areas: I start by studying one-server-per-job multiserver models, and prove the first results on optimal scheduling in that setting. Next, I study the multiserver-job (MSJ) model, where different jobs require different amounts of resources to be served. I prove the first characterization of mean response time for any scheduling policy in the MSJ model, as well as the first optimality results. Finally, I study the effects of scheduling on the tail of response time, rather than mean response time. I invent a novel scheduling policy, Nudge, which I prove to be the first policy to outperform FCFS’s asymptotic tail of response time. This extended abstract briefly outlines the main contributions of my thesis, describing my novel performance bounds and optimality results in each of these three areas. © 2024 Association for Computing Machinery. All rights reserved.

关键词： Stochastic systems

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