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4th International Conference on Expert Clouds and Applications, ICOECA 2024

作者： Arepalli, Gopi Suresh Bhavana, Paluvai Krishna, Yakkanti Venkata Sai Surendrababu, Chittibomma Lakireddy Bali Reddy College of Engineering Dept. of computer Science and Engineering Mylavaram India Dept. of Computer Science and Engineering Mylavaram India

ISBN: (纸本)9798350385793

UPI frauds have led to increased use of biometrics for personal identification to ensure security and accuracy. Biometric UPI based on iris recognition technology improves customer service by providing a safe and paperless environment. This system replaces the existing UPI pin and affords protection to the UPI payment from cyber-attacks. During enrollment, genuine user's iris samples and UPI ID are retained in the database, and transactions are completed by capturing and matching iris patterns by using G-6 iris recognizer and biometric Framework. This innovative technology has revolutionized the way to make payments and has significantly reduced the risk of fraudulent activities. With biometric UPI, customers can enjoy a seamless experience when making transactions. The use of biometrics also ensures that there is no chance of identity theft or unauthorized access to sensitive information. © 2024 IEEE.

关键词： Authentication Protocol

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Trust Based Cooperative Secure Routing Protocols for Internet of Things (IoT) 2

Trust Based Cooperative Secure Routing Protocols for Interne...

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2nd International Conference on Recent Trends in Microelectronics, Automation, Computing, and Communications Systems, ICMACC 2024

作者： Mekala, Sagar Ramji, Bhukya CVR College of Engineering Dept. of Computer Science and Engineering Hyderabad India Kakatiya Institute of Technology and Science Dept. of Computer Science and Engineering Hanmakonda India

ISBN: (纸本)9798350366570

The Internet of Things has rapidly emerged and continues to create services, software, sensors-embedded devices, and protocols. IoT allows physical objects to communicate, exchange information, and make decisions while performing critical jobs. The Internet of Things is enhanced by wireless sensor networks, which serve as a continuous layer. To gain adoption and spread, Internet of things-based sensor networks must overcome serious general and specialized risks and technological hurdles. The main drawbacks of wireless sensor networks are insufficient resources. Internet of Things (IOT)-based Wireless Sensor Networks have issues in energy consumption, longevity, throughput, routing, and security. The goal of this effort was to enhance network longevity, throughput, packet latency/loss, and better encounters with hostile nodes. Consider wireless energy harvesting in the proposed three-layer cluster-based wireless sensor network routing protocol to enhance network lifespan. The proposed approach is a Trust based Cooperative Secure Routing Protocol (TCSRP) technique with security system to obtain malicious activities of objects and to slant them into blacklist. Finally, experiments were done to test the methodology. It outperforms most traditional routing protocols like AODV, DSR, and DSDV. © 2024 IEEE.

关键词： Network layers

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Cybersecurity Threat Detection Using Deep Learning and Anomaly Detection Techniques

Cybersecurity Threat Detection Using Deep Learning and Anoma...

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2024 International Conference on Knowledge engineering and Communication Systems, ICKECS 2024

作者： Rajendran, T. Mohamed Imtiaz, N. Jagadeesh, K. Sampathkumar, Balaji Rajalakshmi Institute of Technology Department of Computer Science and Engineering Chennai India Nagarjuna College of Engineering and Technology Dept. of Information Science and Engineering Bangalore India Sri Venkateswaraa College of Technology Dept. of Computer Science and Engineering Chennai India Akshaya College of Engineering and Technology Dept. of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350359688

Through the use of combined deep learning and anomaly detection approaches, this research investigates the area of cybersecurity threat detection. The study proves the framework's extraordinary success in recognizing as well as mitigating diverse cyber dangers, including insider threats and zero-day attacks, through practical testing and case studies. The framework establishes itself as a proactive cybersecurity solution adaptive to the changing threat landscape due to its high accuracy of 95%, low amount of false positives 5%, and quick reaction times. The recommendations cover user training, scalability, regulatory compliance, including cross-disciplinary collaboration. Behavioral analysis, AI explainability, IoT security, and countering quantum computing concerns should be the main areas of future research. These revelations help the cybersecurity industry become more resilient and well-prepared. In this research investigation, experiments on threat detection were conducted using deep learning algorithms, with a specific focus on Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs). The findings of the study reveal that a detection accuracy of 93% was achieved by CNN, while a higher accuracy of 96% in threat detection was exhibited by RNN. © 2024 IEEE.

关键词： Zero-day attack

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Recognition of Fingerprint Images using CNN for Cybercrime Detection System 2

Recognition of Fingerprint Images using CNN for Cybercrime D...

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2nd IEEE International Conference on Networks, Multimedia and Information Technology, NMITCON 2024

作者： Kavitha, T. Patil, Bhimaraya Saraswathi, S. Rajarajeswari, S. Patil, Anita New Horizon College of engineering Dept. of Computer Engineering Bangalore India Sri Sivasubramaniya Nadar College of Engineering Dept. of Computer Science Engineering Chennai India Vellore Institute of Technology School of computer science and engineering Vellore India City Engineering College Dept. of Information Science Bengaluru India

ISBN: (纸本)9798350372892

Fingerprint identification plays a crucial role in crime detection, but manual analysis is time-consuming and error-prone. This research aims to develop an automated system for accurate and efficient fingerprint matching using machine learning. The proposed approach involves image acquisition, preprocessing, feature extraction, and classification. Fingerprint images are enhanced, binarized, and segmented. Minutiae (ridge endings and bifurcations) are extracted as features. A convolutional neural network (CNN) is trained on a dataset of fingerprint images for classification. By comparing algorithms, Random Forest achieved over 8 1 % accuracy, outperforming Decision Trees (nearly 75% accuracy). The trained CNN model can accurately match fingerprints from crime scenes with potential suspects, providing reliable evidence. The developed system offers law enforcement agencies an automated and accurate fingerprint identification tool, improving crime-solving efficiency and reducing manual effort. The proposed approach showcases the potential of machine learning in forensic applications. © 2024 IEEE.

关键词： Random forests

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Stress Level Analysis Using EEG Signals: A Comparative Study of EEGNET and CNN Deep Learning Models 5

Stress Level Analysis Using EEG Signals: A Comparative Study...

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5th IEEE Global Conference for Advancement in Technology, GCAT 2024

作者： Khanaganni, Anulaxmi S. Monisha, H.M. Kumar, R. Ashok Bms College of Engineering Dept Information Science and Engineering Bangalore India Bms College of Engineering Dept Computer Science and Business Systems Bangalore India

ISBN: (纸本)9798350376685

Understanding the brain's response to stress is pivotal for advancing mental health monitoring technologies. In this research paper, we leverage deep learning techniques to predict stress levels using electroencephalography (EEG) data from 10 students. This data captures brain activity during both rest and stress-induced conditions, with the stress induced through a visually demanding Stroop test. We focus on categorizing stress levels into four distinct categories: no stress, low stress, medium stress, and high stress. For analysis, we employ two advanced deep learning models, EEGNet and Convolutional Neural Network (CNN), which have demonstrated significant potential in managing the complexities of EEG data and identifying intricate patterns related to different stress *** methodology in this study includes rigorous data preprocessing, segmentation, and labeling to ensure the reliability and accuracy of the model training process. The EEG data from the 10 students are combined into a single dataset, with 70% allocated for training and 30% for testing. This approach ensures a robust evaluation of the models' predictive capabilities. We systematically compare EEGNet and CNN based on accuracy, precision, recall, and other relevant metrics. Our goal is to determine which model more effectively predicts stress levels and to understand their respective strengths and limitations. This comparative analysis provides valuable insights and paves the way for future applications in real-time stress monitoring. © 2024 IEEE.

关键词： Convolutional neural networks

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Identification of Therapeutic Plants Using Machine Learning 2

Identification of Therapeutic Plants Using Machine Learning

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2nd International Conference on Emerging Trends in Information Technology and engineering, ic-ETITE 2024

作者： Rohini, S. Keerthana Shree, M.U. Jayalakshmi, R. Malathi, M. Jabasheela, L. Panimalar Engineering College M.E Computer Science and Engineering Tamil Nadu Chennai India Panimalar Engineering College B.E Computer Science and Engineering Tamil Nadu Chennai India Panimalar Engineering College Dept of Computer Science and Engineering Tamil Nadu Chennai India Panimalar Engineering College Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350328202

It takes a lot of work to develop a computerized method for classifying plants for medicinal purposes. There are countless kinds of plants found in India, and each one has its own unique set oftherapeutic advantages. Background information is necessary for manual identification and categorization because it is difficult for people to recall the proper titles of all plant species and their *** is crucial to preserve these medicinal plants since doing so will benefit several industries, including medicine, botanical research, and taxonomic studies, among others. Current technology cannot match thediversity of medicinal herb species that existin India. Utilizing textural traits that are crucial for recognizing and classifying leaves, the recommended technique makes itsimpler to categorize medicinal plants. The feature extraction, categorization, and picture enhancement stages are the three main components of the suggested method. The qualities that could be matched betweenthe leaf images were gathered using digital image processing methods after cell phones were used to snap the leaf photos. The CNN classifier is then used to create a machine learning classifier. © 2024 IEEE.

关键词： Machine learning

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Content Based Classification of Short Messages using Recurrent Neural Networks in NLP

Content Based Classification of Short Messages using Recurre...

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2024 International Conference on Artificial Intelligence, computer, Data sciences, and Applications, ACDSA 2024

作者： Bennet, Deepthi Tabitha Bennet, Preethi Samantha Thiagarajan, Priya Sundarakantham, K. Thiagarajar College of Engineering Dept. of Applied Mathematics and Computational Science Madurai India Thiagarajar College of Engineering Dept. of Computer Science and Business Systems Madurai India Thiagarajar College of Engineering Dept. of Computer Science and Engineering Madurai India

ISBN: (纸本)9798350394528

Short message spam poses a significant threat for all mobile phone users, as it can act as an efficient tool for cyberattacks including spreading malware and phishing. Traditional anti-spam measures are only minimally effective against modern spammers. Intelligent analysis of the content to categorise the messages as Spam (unwanted and unsolicited) or Ham (useful messages) is therefore essential to safeguard the user from such attacks. Various artificial intelligence (AI) techniques are proving to be useful in the analysis of the content of such short messages to classify and filter spam. We have trained, validated and tested seven such AI techniques on the SMS spam collection dataset to identify the best model for designing and developing a content based classification system. Recurrent Neural Networks (RNN) have shown the highest performance metrics (Test Accuracy: 99.28%) and hence our proposed system includes a RNN model for classification. A web app of this system has also been deployed where a single SMS can be input and the designed system can classify it as Spam or Ham. The designed system is compared against existing systems and is found to be significantly better. © 2024 IEEE.

关键词： Recurrent neural networks

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Fire and Smoke Detection Using Deep Learning 2

Fire and Smoke Detection Using Deep Learning

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2nd IEEE International Conference on Networking and Communications, ICNWC 2024

作者： Divya, B. Kavitha, S. Muthu Pandeeswari, R. Sreenidhi, R. Ponvaishnavi, M.R. Loshine, S. Sri Sai Ram Institute of Technology Dept of Computer Science and Engineering India India Rajalakshmi Engineering College Dept of Computer Science and Engineering Thandalam India

ISBN: (数字)9798350365269

ISBN: (纸本)9798350365269

Of late, wildfires and commercial fires like fires in a shopping complex, firework factories, and industries, continue to cause extensive destruction throughout the world, frequently causing human fatalities. The solution is divided into two systems, where the first system focuses on the wildfire and the second system focuses on commercial fires. Identifying the fire and smoke correctly plays an important role. Gradient-weighted Class Activation Mapping (Grad-CAM) is used to identify the smoke region in the image. This algorithm is used to assure the smoke or fire in the image. The algorithms like ResNet, CNN (Convolutional neural network), and VGG16 (Visual Geometry Group) algorithms are used in transfer learning. This aims to increase the accuracy of the detection of fire and smoke in forests to avoid mishaps. The GradCAM is an algorithm that finds out the positive score in the image. This confirms the smoke in the image using the heat map generation. The ReLU activation function is used to show the positive pixels. The saliency map is also used in finding out the difference between smoke and fog in case of wildfire. In the case of the building fire shopping complex, firework shops, factories, and industries would also require a better solution because the detection is easier but the rescue process is more difficult in such places. The same procedure is followed for detecting and finding the origin of the fire as the wildfire model. The YOLOv8 algorithm is used for real-time analysis of the building. The model constantly looks for the unusual behavior of the environment and this in turns induces the notification. The additional feature is added to the model for rescue purposes. Fire control and evacuation have become more complex and hence the solution to such a problem is not yet brought into consideration. Even after detecting the fire, most of the people die due to the poor rescue process. This is because the rescue team cannot find the people inside the buildi

关键词： Smoke

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Remora optimization algorithm-based optimized node clustering technique for reliable data delivery in VANETs

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International Journal of Intelligent Networks 2022年 3卷 74-79页

作者： Konduru, Swathi Sathya, M. Department of Computer Science Pennsylvania State University PA16801 United States Department of Information Science and Engineering AMC Engineering College Bengaluru India

Vehicular ad hoc Network (VANET) is one of the recently growing trends which motivate the provision of several service providers in the urban areas. In VANETs, the vehicles represent the nodes in the network topology that needs to guarantee better cooperation when there is a higher node density. Moreover, the problem of determining an optimal route and achieving network scalability is a herculean task. In this context, the incorporation of a potential clustering algorithm has the possibility of improving the road safety and facilitating a reliable option of promoting message routing. The clustering protocols are determined to be the ideal candidate for solving the problems of network scalability to guarantee reliable data dissemination. In this paper, Remora optimization algorithm-based Optimized Node Clustering (ROAONC) Technique is proposed for node clustering in VANETs to achieved optimal CH selection process. This ROAONC scheme was proposed for minimizing network overhead in the scenarios of unpredictable node density. The simulation results of this ROAONC scheme confirmed better performance in terms of transmission range, node density, network area and number of clusters compared to the competitive ant colony, grey wolf, grasshopper, and dragonfly optimization algorithm-based clustering protocols. © 2022 The Authors

关键词： Vehicular ad hoc networks

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Reinforcement Learning for Adaptive Traffic Signal Control Using Deep Q-Networks 1

Reinforcement Learning for Adaptive Traffic Signal Control U...

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1st International Conference on Sustainable Energy Technologies and Computational Intelligence, SETCOM 2025

作者： Bohra, Asha Rani Selvi, Thamarai Vishal, V. Sunkara, Pawan Sai Sunku, Balaji Jignesh, Bhavsar Nitya New Horizon College of Engineering Dept. of Computer Science and Engineering Karnataka Bengaluru India New Horizon College of Engineering Dept. of Information Science and Engineering Karnataka Bengaluru India

ISBN: (纸本)9798331520540

Traffic congestion is a critical issue in urban areas, contributing to increased travel time, fuel consumption, and environmental pollution. Traditional traffic signal control methods, such as fixed-time systems, cannot adapt to real-time changes in traffic conditions. This paper presents a novel approach using Deep Q-Networks (DQN) to control traffic signals based on realtime traffic data dynamically. Our simulation environment models a four-way intersection, where vehicle densities and wait times are continuously monitored. The DQN agent optimizes green light durations by minimizing vehicle wait times and maximizing traffic throughput. Extensive simulation results demonstrate that our system significantly reduces congestion and improves traffic flow compared to traditional fixed-time systems. Furthermore, the approach shows potential for real-world applications in urban traffic control systems, particularly in smart cities equipped with Internet of Things (IoT) devices. © 2025 IEEE.

关键词： Traffic congestion

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