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6th International Conference on Inventive Computation Technologies, ICICT 2023

作者： Gotti, Rajeswari Polagani, Abhiram Posina, Gagana Sai Lakshmi Veerapaneni, Srihari Prasanth, Talluri Dept. of Computer Science and Engineering Seshadri Rao Gudlavalleru Engineering College Gudlavalleru India Dept. of Computer Science and Engineering Seshadri Rao Gudlavalleru Engineering College Machilipatnam India

ISBN: (纸本)9798350398496

A MANET facilitates the creation of a wireless connection between two devices without relying on any fixed infrastructure or centralized administration. Security is the main drawback in MANET's. As a result, intruder can easily connect to the network. To overcome this problem an efficient Intrusion Detection System should be built, in such a way the IDS can identify the intrusion. To detect the intruder like blackhole attack, this research study has proposed a novel technique in which a network consisting of 25 nodes is simulated and connected with TCP and UDP connection and collect different characteristics from the trace file. Now these characteristics are analyzed by using various machine learning algorithms. © 2023 IEEE.

关键词： Intrusion detection

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UAV-based Firefighting by Multi-agent Reinforcement Learning 13

UAV-based Firefighting by Multi-agent Reinforcement Learning

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13th International Conference on computer and Knowledge engineering, ICCKE 2023

作者： Tanha, Reza Shami Hooshmand, Mohsen Afsharchi, Mohsen Dept. Computer Science and It Zanjan Iran University of Zanjan Dept. Electrical and Computer Engineering Zanjan Iran

ISBN: (纸本)9798350330151

Wildfire in the forest leads to a huge amount of financial and human losses. That is it causes damage to the forest and the life of firefighters. To reduce the amount of such damage, unmanned aerial vehicles are among the best options to do the job of firefighting. Their orchestration of effective fire extinguishing is a must and essential. This work proposes a multi-agent reinforcement learning method using an actor-critic structure to extinguish the fire. While the learning is centralized, their execution is decentralized. Moreover, we define two models of fire spreading based on the discrete-time Markov process. The results show that the proposed method outperforms the state-of-the-art methods. © 2023 IEEE.

关键词： Reinforcement learning

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LeXNet++: Layer-wise eXplainable ResUNet++ framework for segmentation of colorectal polyp cancer images

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Neural Computing and Applications 2025年第1期37卷 213-229页

作者： Das, Surajit Khan, Soumya Suvra Sengupta, Diganta Debashis, De Dept. of Information Technology Meghnad Saha Institute of Technology Behind Urbana Complex Near Ruby General Hospital Anandapur Rd Uchhepota Kolkata700150 India Dept. of Computer Science & Engineering Meghnad Saha Institute of Technology Behind Urbana Complex Near Ruby General Hospital Anandapur Rd Uchhepota Kolkata700150 India Dept. of Computer Science & Engineering Heritage Institute of Technology Chowbaga Road West Bengal Anandapur Kolkata700107 India Dept. of Computer Science & Engineering Maulana Abul Kalam Azad University of Technology West Bengal NH-12 Simhat Haringhata West Bengal Nadia741249 India

Colorectal polyps are benign lesions that develop in the colon and can progress to cancer if left untreated. Clinical observations from medical images are often preferred over computational results due to the lack of trust in the machine learning models, thereby posing serious challenge for the explainability of the results. In order to computationally diagnose colorectal polyps from cancerous images and explain the results, we propose a Layer-wise eXplainable ResUNet++ (LeXNet++) framework for segmentation of the cancerous images, followed by layer-wise explanation of the results. We utilize a publicly accessible dataset that contains of 612 raw images with a resolution of 256×256×3 and an additional 612 clinically annotated and labeled images with a resolution of 256×256×1, which includes the infected region. The LeXNet++ framework comprises of three components—encoder, decoder and the bridge. The encoder and the decoder components each comprise of four layers. Each of the four layers in the encoder and the decoder comprises of 14 and 11 internal sub-layers, respectively. Among the sub-layers of the encoder and the decoder, there are three 3×3 convolutional layers with an additional 3×3 convolution-transpose layer in the decoder. The output of each of the sub-layers has been explained through heatmap generation after each iteration which have been further explained. The encoder and the decoder are connected by the bridge which comprises of three sub-layers. The results obtained from these three sub-layers have also been explained to inculcate trust in the findings. In this study, we have used three models to segment the images, namely UNet, ResUNet, and proposed LeXNet++. LeXNet++ exhibited the best result among the three models in terms of performance;hence, only LeXNet++ was explained layer-wise. Apart from explanation of the results fetched in this study, the performance of the proposed explainable model has been observed to be 2% greater than the existing poly

关键词： Image segmentation

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Smart Attendance Management System Using Face Recognition 1

Smart Attendance Management System Using Face Recognition

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1st International Conference on Advances in computer science, Electrical, Electronics, and Communication Technologies, CE2CT 2025

作者： Rawat, Shivam Goyal, Shweta Rawat, Nikita Graphic Era Hill University Dept. Computer Science & Engineering Dehradun India Graphic Era deemed to be University Dept. of Electrical Engineering Dehradun India

ISBN: (纸本)9798331518578

Managing attendance in educational institutions is often a time-consuming and error-prone task, with traditional methods like roll calls or sign-in sheets being inefficient and susceptible to proxy attendance. This paper presents a Smart Attendance Management System that leverages facial recognition technology to automate the attendance process. By using cameras to capture images of students, the system recognizes their faces and marks their attendance in real time. Developed in Python, the system employs the K-Nearest Neighbours (KNN) algorithm for accurate face recognition. Once attendance is recorded, it is stored in a CSV file and can be visualized using the flask web application. The proposed system significantly reduces manual effort, ensures accuracy, and operates efficiently in real-time environments. © 2025 IEEE.

关键词： Nearest neighbor search

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The Effects of Selected Preferred Music on Perceived Emotions through Audiovisual Stimuli 9

The Effects of Selected Preferred Music on Perceived Emotion...

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9th IEEE/ACIS International Conference on Big Data, Cloud Computing, and Data science, BCD 2024

作者： Thaiwong, Thunrada Fukumoto, Makoto Graduate School of Computer Science and Engineering Fukuoka Institute of Technology Fukuoka Japan Fukuoka Institute of Technology Dept. of Computer Science and Engineering Fukuoka Japan

ISBN: (纸本)9798350394191

Music surrounds us, and there is no denying that music in visual media can shape and evoke emotions. Yet, understanding how musical preference influences emotions through audio and visual stimuli remains an important task. To address this task, we investigated the role of musical preference effect on perceived emotions induced through music and visual stimuli (i.e., animation), using a 7-point scale for valence-arousal ratings and physiological responses in electroencephalogram (EEG) band power. The perceived emotions are categorized into four states: happiness, calmness, fear, and sadness. One emotional state contains 4 sessions: (1) Preferred Music, (2) Unfamiliar Music, (3) Preferred Music+Animation, and (4) Unfamiliar Music+ Animation. Behavior-wise, the rating results showed that the presence of preferred music resulted in higher perceived valence ratings, particularly in happiness. However, no stimulus had a significant effect on perceived arousal ratings and satisfaction ratings. Physiologically, the EEG indexes showed that the presence of preferred music appears to affect an increase in alpha power across various emotions except sadness, whereas unfamiliar music seems to affect beta power, particularly in happiness and calmness. Overall, these findings supported that musical preference is an affective factor reflecting the levels of valence and alpha power in EEG. Nevertheless, our findings did not confirm a significant difference between only preferred music and combining it with animation. Interestingly, we also found that participants were more likely to be attracted to and perceive positive (i.e., happiness and calmness) emotions easily through preferred music and/or visual stimuli than negative (i.e., fear) emotions. © 2024 IEEE.

关键词： Animation

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Exploring emerging spreaders through GCN-based link prediction and a novel centrality method 17

Exploring emerging spreaders through GCN-based link predicti...

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

作者： Nandi, Suman Maji, Giridhar Dutta, Animesh National Institute of Technology Dept. of Computer Science and Engineering West Bengal Durgapur India Asansol Polytechnic Dept. of Electrical Engineering West Bengal India

ISBN: (纸本)9798331531195

Exploring influential spreaders and predicting missing links in complex networks is essential for understanding and effectively controlling network dynamics. This paper presents a Graph Convolutional Network (GCN)-based link prediction method to estimate the probability of future link formation. We incorporate node features that capture local and global topological connectivity structures and feed these into the GCN model, where convolutional layers aggregate neighboring information and transform node features. This approach enables the model to capture structural patterns by integrating local and global information from neighboring nodes. In the final layer, the GCN model computes a prediction score representing the likelihood of an edge's existence, using insights gained during training. Finally, considering the predicted links, we update the network structure and introduce a novel centrality method called Emerging Spreader Centrality (ESC) to identify emerging spreaders within this augmented network. We conduct two separate experiments to evaluate the performance of the GCN-based link prediction and the ESC method, comparing their effectiveness with various state-of-the-art methods. Results demonstrate that our approach not only effectively predicts future links but also identifies emerging spreaders in the augmented networks. © 2025 IEEE.

关键词： Prediction models

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Unveiling DNS Spoofing Vulnerabilities: An Ethical Examination Within Local Area Networks 26

Unveiling DNS Spoofing Vulnerabilities: An Ethical Examinati...

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26th International Conference on computer and Information Technology, ICCIT 2023

作者： Jony, Asaduzzaman Islam, Muhammad Nazrul Sarker, Iqbal H. Dept. of Computer Science and Engineering Dhaka Bangladesh Dept. of Computer Science and Engineering Dhaka Bangladesh Edith Cowan University PerthWA-6027 Australia

ISBN: (纸本)9798350359015

Nowadays DNS spoofing has become one of the terrible attacks which is triggered by exploiting IP address conflict detection vulnerability on the DHCP server side. DHCP server running on latest operating systems can detect IP address conflict which makes proposed attack ineffective. On the other hand, DNS spoofing attack using server side DNS cache poisoning is very difficult and unfeasible task as the public DNS servers are very secured and deployed behind several security appliances. Again, DNS spoofing attack is initiated by poisoning the client's DNS cache, which is uncertain as it requires a malware to be installed in target user's computer. Therefore, the objective of this study is to introduce an innovative approach for ethically launching a DNS spoofing attack within a Local Area Network (LAN) for illustrating the vulnerabilities and security flaws of the DNS protocol. To achieve this objective, attacker introduces a rogue DHCP server in the LAN and forcefully redirects user's traffic to a malicious or rogue default gateway. Secondly, a python (scapy) based DNS spoofing engine is deployed in the rogue DHCP server that tampers the DNS responses replied by the DNS server. The scapy alters the IP address of legitimate website with an IP address of malicious or phishing website which is managed by the attacker and looks like legitimate website. As a result, compromised user is redirected to that identical looking phishing website and becomes victim of phishing attack. Finally, the effectiveness and validation of the proposed attack were demonstrated using the Kali Linux and multi-vendor network emulation software (EVE-NG). © 2023 IEEE.

关键词： DNS Spoofing Ethical Considerations LAN security Phishing Python Scapy Rogue DHCP Wireshark

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Automated Attendance Management System 5

Automated Attendance Management System

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5th IEEE International Conference on Advances in Computing, Communication Control and Networking, ICAC3N 2023

作者： Goyal, Pranshu Nigam, Vishwas Khan, Shahnaw A.J. Singh, Shashi Kant Galgotias College Of Engineering And Technology Dept. Of Computer Science And Engineering India

ISBN: (纸本)9798350330861

the manual process of recording and managing attendance in educational institutions and organizations is often time-consuming, prone to errors, and inefficient. To address these challenges, there is a growing need for automated attendance management systems. This research paper presents the design and implementation of an Automated Attendance Management System (AAMS) using modern technologies. The AAMS utilizes various technologies such as biometric recognition, RFID (Radio Frequency Identification), and computer vision to accurately and efficiently track attendance. The system aims to simplify the attendance recording process for both students and instructors, reduce administrative burden, and improve overall efficiency. The proposed AAMS incorporates biometric recognition techniques, such as fingerprint scanning or facial recognition, to identify and verify the identity of individuals. © 2023 IEEE.

关键词： Efficiency

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Cross-Domain Adaptation Through Adversarial Teaching for Unmanned Ariel Vehicles in Smart Cities 22

Cross-Domain Adaptation Through Adversarial Teaching for Unm...

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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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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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