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2nd International Conference on Computational Intelligence and Network Systems, CINS 2024

作者： Singh, Amritpal Singh, Amrinderpreet Batra, Shalini Department of Computer Science and Engineering National Institute of Technology Jalandhar Jalandhar India Department of Computer Science and Engineering Thapar Institute of Engineering & Technology Patiala India

ISBN: (纸本)9798331504106

Analysing patterns/trends and associations from heterogeneous data coming at varied speeds and formats require data structures which can handle large and dynamic data efficiently. Bloom Filter (BF), a probabilistic data structure, can be considered as one of the alternatives as it is space efficient and handles membership query effectively. To accommodate the dynamic data sets in the BF when ever the Fill Ratio (FR) exceeds the defined limits, two major approaches have been discussed in the literature: increasing the number of BFs as the data increases and refreshing the existing BF by deleting the staled data. In this paper, a novel approach motivated from Double probing named (DH-FRP) has been proposed to compute FR using augmented array to sample bit locations from parent BF. The augmented array keeps track of the sampled bit values at any given time and stores at contiguous locations for effcient bit counting and FR calculation. Markov Bound has been used to determine whether to recompute the step size or not. Since the rate of dynamic data coming from various sources may be uniform or non uniform, both cases have been considered and FR has been calculated for standard BF and Counting BF. It has been experimentally proved that the proposed scheme outperforms the given state-of-art techniques in terms of FR calculation time and accuracy. ©2024 IEEE.

关键词： Markov processes

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Brain Tumour Detection using MRI Images 1

Brain Tumour Detection using MRI Images

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1st International Conference on Intelligent Systems and Computational Networks, ICISCN 2025

作者： Jayashree Mallick, Ahmed Faraz Das, Ashutosh Singh, Abhiraj Shivaprakash, T. Preethi Salian, K. Nitte Meenakshi Institute of Technology Dept of Computer Science and Engineering Bangalore India Nitte Meenakshi Institute of Technology Dept of Computer Science and Engineering India Vijaya Vittala Institute of Technology Dept of Computer Science and Engineering Bangalore India Nmamit Dept of Information Science and Engineering Nitte Udupi India

ISBN: (纸本)9798331529246

An essential diagnostic technique for brain tumors, Magnetic Resonance Imaging (MRI) enables early detection and improved patient outcomes. However, manual interpretation of MRI scans can be time-consuming and subject to human error. Deep learning has revolutionized medical image analysis by providing automated and accurate classifications, significantly enhancing diagnostic processes. This study leverages the pre-trained Convolutional Neural Network (CNN) model Xception to classify MRI brain data, demonstrating its effectiveness in identifying brain tumors. The model was trained and validated using a diverse dataset, achieving a high test accuracy of 98.78% and training accuracy of 99.96%. These findings highlight the potential of the Xception model in supporting clinical decision-making and reducing diagnostic workloads. The study contributes to the field by showcasing Xception's implementation, dataset preparation, model design, training, and evaluation. Furthermore, it emphasizes the importance of using deep learning techniques for reliable and efficient brain tumor classification, setting the groundwork for future advancements in automated diagnostic systems. © 2025 IEEE.

关键词： Convolutional neural networks

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Deep Learning Strategies for Identifying Machine-Generated Text 19

Deep Learning Strategies for Identifying Machine-Generated T...

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19th International Conference on Ubiquitous Information Management and Communication, IMCOM 2025

作者： Yadagiri, Annepaka Pakray, Partha Computer Science & Engineering National Institute of Technology Silchar India

ISBN: (纸本)9798331507817

Generative AIs like LLMs are now accessible to the general public. For example, students can utilize these tools to create essays or complete theses. However, how is a teacher supposed to determine if a text was composed by the student or an AI? Using deep learning techniques, we investigate novel and classic approaches for detecting text created by artificial intelligence. We also study the more complex instance when the AI is asked to write the text in a way that a human would not recognize as AI-generated, as we discovered that categorization is more challenging in this scenario. For our studies, we used llm-detect-ai-generated text from the Kaggle competition dataset, which included texts written by students and texts produced using different LLMs. Our top systems achieve an accuracy of 0.98% and F1 scores of more than 0.98% in classifying simple and complex texts produced by humans and AI-Genereted. The systems combine features such as TF-IDF vectorization, word2Vec, and word embedding properties. Our findings demonstrate that these additional characteristics significantly enhance the performance of several classifiers. Compared to deep learning models, our best-performing model for detecting AI-generated text outperforms even the fine-tuned ROBERTA-Open-AI classifier, achieving an accuracy of 0.98%. This underscores the efficacy of our proposed approach in distinguishing between human and AI-generated content. © 2025 IEEE.

关键词： Adversarial machine learning

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Proposed Solution for Currency Classification and Fake Currency Detection Using Machine Learning 4

Proposed Solution for Currency Classification and Fake Curre...

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4th International Conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Narale, Dnyaneshwari. B. Pujar, Anita. M. Pardeshi, K.R. Walchand Institute of Technology Computer Science and Engineering Solapur India

ISBN: (纸本)9798331523923

The rapid advancement of digital technologies has greatly impacted financial systems, creating challenges in accurately classifying currencies and detecting counterfeits in various real-world scenarios. Traditional approaches, such as manual inspections and specialized tools like UV light detectors, tend to be time-consuming, expensive, and less effective against sophisticated counterfeiting methods. Recent developments in machine learning and image processing have shown potential, utilizing supervised machine learning models. Nonetheless, issues remain in managing variations due to wear, folds, inconsistent lighting, and different currency designs. This research introduces a robust machine learning-based system for classifying currencies and detecting counterfeit bills. By combining advanced image processing techniques to extract essential features (like texture, edges, and color histograms) with a hybrid model that integrates CNN, Random Forest, and XGBoost, the proposed framework tackles challenges related to real-world variability and subtle signs of counterfeiting. The goal of the system is to achieve high accuracy and reliability in identifying counterfeit notes while ensuring it can scale and adapt to new counterfeiting methods. © 2025 IEEE.

关键词： Adversarial machine learning

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Efficient Cache Retrieving Scheme in VANET via Online Reinforcement Learning

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IEEE Transactions on Vehicular technology 2025年第6期74卷 9506-9519页

作者： Tan, Zhenhao Chen, Bin Li, Ruidong Yang, Lei Xu, Xiaohua University of Science and Technology of China School of Computer Science and Technology Hefei230026 China Kanazawa University Institute of Science and Engineering Ishikawa920-1192 Japan South China University of Technology School of Software Engineering Guangdong510640 China

The implementation of intelligent driving technology is inseparable from reliable wireless communication and efficient cache scheduling in Vehicle Ad-hoc Networks (VANETs). The purpose of cache scheduling in VANETs is to provide efficient and fast data services to vehicles. In response to difficult to handle wireless communication interference and high requirements for communication delay and energy consumption optimization in vehicle-road cooperation, we propose a new communication model of "Retrieving-Cast"to simplify the cache scheduling process oriented by data requirements. In V2I mode, consider the uplink of all roadside units(RSUs) retrieving data from vehicles, and prioritize data retrieving based on factors such as the number, density, and urgency of vehicles within the communication coverage range of RSUs. We hope that as many high-priority RSUs as possible to complete cached data retrieving as soon as possible under wireless communication interference. To overcome the limitations of traditional heuristic algorithms with low scheduling efficiency and deep learning methods requiring a large amount of training data, we propose a double-online reinforcement learning method, which efficiently solves the cache retrieval scheduling problem. Through experiments and analysis, our method achieved better scheduling results compared to traditional approximation algorithms and single greedy Deep Q-learning methods. The designed data transformation method also addresses the issue of requiring a large amount of training data, enabling rapid computation of scheduling requests in the network at an extremely low training cost, meeting the low-latency computation requirements in VANETs. © 1967-2012 IEEE.

关键词： Reinforcement learning

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A Hybrid IoT and Machine Learning Approach for Crop Recommendation Using a Voting Ensemble Model 2

A Hybrid IoT and Machine Learning Approach for Crop Recommen...

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2nd IEEE International Conference on Integrated Circuits and Communication Systems, ICICACS 2024

作者： Suresh, A. Geetha Vani, B. Lavanya, M. Kumar, R.G. Siddharth Institute of Engineering & Technology Department of Computer Science and Engineering A.P India

ISBN: (纸本)9798350317558

Agriculture is a key component of a nation's economy. In the traditional method, farmers rely on experience to determine the ideal climate and environment for a given crop. technology is advancing, and while the system depends on it to some extent, improvements are still required. This study presents an Internet of Things (IoT) based crop recommendation system that uses Machine learning (ML) to use the value that IoT devices collect from the soil. Initially, we utilized baseline ML classifier models like Support vector machine (SVM), K-Nearest Neighbor(KNN), Random forest (RF), and Random forest (NB).Then we employed an ensemble model to detect the suitable crops. We employed several IoT sensors in our research. The DHT11 sensor collects real-time environmental data, such as temperature and humidity. During crop recording, vital soil parameters like potassium and phosphorus - which are essential for crop growth - are measured using an NPK sensor, a pH sensor, and a rain sensor. These sensors can be seamlessly integrated by the IoT device, offering an effective way to collect crucial agricultural data. The Voting Base Ensemble performs exceptionally well in terms of precision, recall, and F1 score measures. The voting Base Ensemble algorithm improves metrics such as F-score, recall, and precision by up to 98%. A thorough evaluation of soil conditions is made possible by the integration of various sensors, and the application of an ensemble model enables accurate crop recommendations based on environmental factors. © 2024 IEEE.

关键词： Agriculture Crop Recommendation Ensemble IoT Machine Learning

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Challenge Response Protocol using Multivariate Polynomial Cryptography

Challenge Response Protocol using Multivariate Polynomial Cr...

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2025 IEEE International Students' Conference on Electrical, Electronics and computer science, SCEECS 2025

作者： Jaiswal, Chandra Keshwar Sharma, Arpit Chaturvedi, Atul Allenhouse Institute of Technology Computer Science and Engineering Kanpur India

ISBN: (纸本)9798331529833

The objective of this paper is to define the meaning of a multivariate public key using two or more multivariate encrypted parties A and B which targets to improve the existing multivariate public key encryption. Two polynomials are created by a central authority for the protocol's setup phase. The client generates a public key using a secret key, then uses his secret key to respond to a challenge the server issues and submits it to the server. The response is hashed to ensure its security. Any design, including ones with physically unclonable functions, can use this multivariate polynomial cryptography scheme since it is appropriate for post-quantum computing and quantum resistant encryption. This protocol, which protects the CCA technique of post-quantum communications, validated the promise of multivariate polynomials for information security. The work makes an effort to portray polynomial implementation and polynomial contribution as security-sensitive jobs. © 2025 IEEE.

关键词： Quantum computers

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Enhancing Mental Health Support: An LLM-Based Prompt engineering Method 8th

Enhancing Mental Health Support: An LLM-Based Prompt Enginee...

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8th World Conference on Smart Trends in Systems Security and Sustainability, WorldS4 2024

作者： Kaysar, Md Nadim Shiramatsu, Shun Computer Science & Engineering Nagoya Institute of Technology Nagoya Japan

ISBN: (纸本)9789819793266

Mental health disorders are conditions of the mind that cause changes in emotion, thought, and behavior. It can be connected with distress and problems functioning in social, professional, or family activities. This study tries to address this issue and improve the user’s mental health situation by using the proposed LLM-based Agent system. Moreover, this research study tries to determine the user’s current situation through text-based conversation and provide suggestions to improve his mental health problems. Therefore, we developed this system by using LLM-based nCoT (nested Chain of Thought) prompt engineering. In the result section, we sequentially obtained the following: a patient goal achievement score (Average: 5.42), patient Satisfaction score (Mean: 5.57), system response Correctness score (Mean: 6.09), and system Engagement Score (Mean: 6.16). In addition, we analyzed the based CTRS and we obtained all item’s averages (Mean: 5.70), and the standard deviation (Mean: 0.91). Moreover, a system usability scale framework was used for the analysis of the usability (Mean Score: 3.60) of our proposed system. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Chatbot CoT Dialogue system LLM Mental Health nCoT

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A Scalable and Secure Facial Recognition Attendance System Leveraging CNN Algorithms for Real-Time Accuracy and Efficiency

A Scalable and Secure Facial Recognition Attendance System L...

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2025 IEEE International Students' Conference on Electrical, Electronics and computer science, SCEECS 2025

作者： Kumar, Aakash Sharma, Jiya Renjith, P.N. Vellore Institute of Technology School of Computer Science and Engineering India

ISBN: (纸本)9798331529833

The facial recognition attendance system represents a modern approach to attendance tracking, designed to improve efficiency, accuracy, and security in various organizational settings. Advanced biometric technologies are leveraged to capture and encode facial features, creating unique digital identifiers for each individual. The methodology employs cutting-edge facial recognition algorithms powered by convolutional neural networks (CNNs) with a 99.5% recognition accuracy rate. These algorithms extract and encode facial vectors with sub-millisecond latency, which are then compared in real-time against a secure, encrypted database of preregistered individuals. Upon a successful match, the system automatically records the attendance, ensuring an accurate and efficient process. With an intuitive web interface built on ReactJS and a backend supported by *** and MongoDB, the system provides seamless access to attendance records and administrative functionalities. The entire attendance recording process is optimized for speed, typically completing within two seconds per individual. Additionally, advanced anti-spoofing measures and integration support with existing HR management systems redefine traditional attendance management practices with a scalable, robust, and secure solution. (Abstract) © 2025 IEEE.

关键词： Efficiency

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The Power of Virtual Reality-Next-Gen Radiology Training 15

The Power of Virtual Reality-Next-Gen Radiology Training

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

作者： Ahmed, Alisha Rajarajeswari, S. Mallegowda, M. Subash, N. Ramaiah Institute of Technology Computer Science Engineering India

ISBN: (纸本)9798350370249

Advancements in medical education are continuously evolving, with virtual reality (VR) emerging as a groundbreaking technology reshaping the landscape of radiology training. This abstract presents a paradigm shift in radiology education through the creation of immersive VR simulations tailored for medical students and professionals. Our VR simulation leverages state-of-the-art graphics, interactive modules, and realistic patient scenarios to provide a dynamic learning environment. Users can navigate through virtual clinics, interact with lifelike patients, and practice diagnostic procedures in a risk-free yet lifelike setting. The incorporation of haptic feedback and spatial audio enhances realism, fostering deeper engagement and skill acquisition. Furthermore, our VR simulation integrates artificial intelligence algorithms for personalized learning pathways, adapting to individual learning styles and providing real-time feedback. By harnessing the power of VR, this simulation not only enhances diagnostic skills but also promotes critical thinking, teamwork, and decision-making under pressure. This abstract explores the transformative impact of VR simulations in revolutionizing radiology education, paving the way for a new era of experiential learning in medical training. © 2024 IEEE.

关键词： Radiology

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