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2nd IEEE International Conference on Data science and Network Security, ICDSNS 2024

作者： Ahamed, H. Riaz Hanirex, D. Kerana Bharath Institute of Higher Education and Research Department of Computer Applications Tamil Nadu Chennai India Bharath Institute of Higher Education and Research Department of Computer Science Tamil Nadu Chennai India

ISBN: (纸本)9798350373110

This study presents a novel approach to enhancing personalized feedback in e-learning platforms through the use of machine learning algorithms. As part of the research process, we compile and examine anonymized student data to develop a model capable of generating personalized text feedback. The model is trained and validated using a comprehensive dataset sourced from various educational resources. In addition, a Streamlit web application has been developed to allow users to provide personalized feedback. An iterative refinement process enhances the model's functionality and intuitiveness. An evaluation of the relevance and quality of the generated feedback is carried out through a manual assessment and the collection of user feedback. The study demonstrates significant contributions to improving student learning outcomes through personalized assistance. This study provides a significant advancement in the field of e-learning by empowering students to take ownership of their education through personalized feedback tailored to their individual needs. © 2024 IEEE.

关键词： Adversarial machine learning

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An Automated Two-Stage Brain Tumour Diagnosis System Using SVM and Geodesic Distance-Based Colour Segmentation

An Automated Two-Stage Brain Tumour Diagnosis System Using S...

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International Conference on Power Engineering and Intelligent Systems, PEIS 2023

作者： Syedsafi, S. Sriramakrishnan, P. Kalaiselvi, T. Department of Computer Applications Kalasalingam Academy of Research and Education Tamil Nadu Krishnankoil626126 India Tamil Nadu Coimbatore641112 India Department of Computer Science and Applications The Gandhigram Rural Institute [Deemed to be University Tamil Nadu Dindigul624302 India

ISBN: (纸本)9789819972159

In the medical profession, brain tumour is a very crucial illness. A brain tumour is an unwanted mass growing in the brain cells;if it’s not prevented, it will eventually cause death. Therefore, tumour diagnosis is essential. Magnetic resonance imaging (MRI) is used to identify the brain tumour quickly. The approach of detecting a brain tumour from human eyesight is quite difficult. The proposed work automatically diagnoses the brain tumour. This proposed technique has two stages: classification and segmentation. The classification stage is used to classify the T2W-MRI images into a tumour and normal using 8 x 8 blocks with gray-level co-occurrence matrix (GLCM) features using a support vector machine (SVM). The second stage segments the FAIR and T1C type MRI images using colour-based segmentation technique. This proposed method uses the BraTS2013 dataset. Classification and segmentation result is calculated by sensitivity, specificity and accuracy. In the segmentation, it additionally uses the dice similarity coefficient (DSC) to find the accuracy. The outcomes denote the proposed method's accuracy of classification as 96.66% and the DSC of segmentation accuracy as 80%. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2024.

关键词： Magnetic resonance imaging

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Blockchain-Enabled Secure and Fair Online Lottery Systems Using Smart Contracts

Blockchain-Enabled Secure and Fair Online Lottery Systems Us...

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2024 IEEE Silchar Subsection Annual Conference, SILCON 2024

作者： Datta, Suseta Mondal, Rituparna Roy, Rajdeep Banerjee, Sourav Biswas, Utpal Narula Institute of Technology Department of Computer Application West Bengal Kolkata India Techno India University Department of Computer Applications West Bengal Kolkata India University of Kalyani Department of Computer Science and Engineering West Bengal Kalyani India Kalyani Government Engineering College Department of Computer Science and Engineering West Bengal Kalyani India

ISBN: (纸本)9798331540821

In the capricious digital dominion, conventional online lottery systems come across consequential impediment regarding transparency, fairness, and security. This paper presents an inventive solution by merging Blockchain technology and smart contracts into online lotteries to take forward these challenges. The preferred disperse implementation capitalizes on Blockchain's decentralized category to covenant the translucency and immutability of lottery transactions, thereby undoing the requisite for trusted interceders. Smart contracts are immersed to automate the entire lottery process-from ticket provisioning to winner selection and payout-certifying agreement to preconceived rules and reinforcing contender trust. Through meticulous perusal and enactment, illustration of how Blockchain technology can transform online lottery systems, providing users with a secure, transparent, and fair platform have been performed. Furthermore, the suggested system's resistance to recurrent deceit and its prospective impression on the online gambling industry are talked about, foregrounding the welfares of a Blockchain-drove technique to online lotteries. © 2024 IEEE.

关键词： Smart contract

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Algorithmic Strategies for Cyber Crime Attack Prevention Harnessing the Power of Convolutional Neural Networks 7

Algorithmic Strategies for Cyber Crime Attack Prevention Har...

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7th International Conference on Circuit Power and Computing Technologies, ICCPCT 2024

作者： Deva Kirubai, J. Christina Priscila, S. Silvia Bharath Institute of Higher Education and Research Department of Computer Applications Tamil Nadu Chennai India Bharath Institute of Higher Education and Research Department of Computer Science Tamil Nadu Chennai India

ISBN: (纸本)9798350372816

Cybercrime is a major concern in today's cyber world, and immediate action must be taken to prevent it. Traditional cybersecurity systems are vulnerable to acclimatization issues, a high percentage of false positive alerts, and so forth. To address these problems, the study proposes a cutting-edge technique that blends CNNs with transmission characteristics for anti-cybercrime policy. The CN-based system is designed to retrieve network traffic data in real-time, with the reliability of identified hostile activity proportional to the minimizing of false alarms. The system includes components such as data collection and preprocessing, CNN architecture design, training, real-time analysis, and adaptation to emerging threats. Superior performance characteristics can be seen in comparison to existing systems, including 0.90-0.92 greater accuracy, 0.89-0.94 precision, 0.97-0.98 recall, and 0.98-0.93 F1-score with a ROC AUC score of 0.97. Furthermore, the proposed system has faster detection speeds (1200 packets per second) and lower false positive rates for various cyber threat types such as malware (0.03), phishing assaults (0.05), and DDoS attacks (0.02). Thus, the proposed system represents a big step forward in the evolution of cyber security, adopting more active threat identification and mitigation, thereby strengthening existing network security and exposing it to fewer threats. © 2024 IEEE.

关键词： Phishing

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Exploring and Evaluating Energy-Efficient Clustering Schemes in AODV: An Implementation and Comparative Study 12th

Exploring and Evaluating Energy-Efficient Clustering Schemes...

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12th International Conference on Soft Computing for Problem Solving, SocProS 2023

作者： Kumar, Ankit Pandy, Saroj Kumar Kumar, Gaurav Singh, Kamred Udham Singh, Teekam Srivastava, Sandeep Choudhury, Tanupriya Department of Computer Engineering and Applications GLA University UP Mathura India School of Computing Graphic Era Hill University Dehradun India Department of Computer Science and Engineering Graphic Era Deemed to be University Dehradun India Department of Computer Science and Engineering Galgotias University Greater Noida India Department of Computer Science and Engineering University of Petroleum and Energy Studies Dehradun India

ISBN: (纸本)9789819732913

Wireless sensor networks play an important part in the monitoring and analysis of the physical environment since they compile data from a wide range of sensors. In this research study, a clustering protocol is presented with the intention of establishing an automated framework for the organization of a wireless sensor network (WSN). The data gathering, processing, and decision-making capabilities of the network are all improved as a result of the implementation of the suggested protocol, which also contributes to the network's increased overall efficiency. In this paper, we analyse the security concerns surrounding MANETs and suggest a few promising new lines of study. Several key and trust management systems have been created and suggested in order to prevent attacks from occurring from outside the MANET system. Additionally, a number of secure MANET routing protocols have been developed in order to prevent attacks from occurring from inside the MANET system. Concerning the detection of intrusions, research has been conducted on a recently developed intrusion detection framework that was developed with MANET in mind especially. In order to address the issues about MANET's level of security, measures of both prevention and detection will be used. The clustering technique that was suggested in this research makes use of a hierarchical, scalable network architecture that is focused on energy optimization. The strategy that has been offered reduces the amount of energy that is wasted while simultaneously increasing the effectiveness of the network in terms of data collection and transmission. This is accomplished by intelligently grouping sensor nodes into clusters according to the amounts of energy they possess and the capabilities they possess. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Routing protocols

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Optimization of Speech Emotion Recognition Through Advanced Ensemble Learning Techniques 9

Optimization of Speech Emotion Recognition Through Advanced ...

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9th International Conference on Communication and Electronics Systems, ICCES 2024

作者： Prathyakshini Prathwini Keerthana Department of Information Science and Engineering Nitte Karkala India Department of Master of Computer Applications Nitte Karkala India Nitte Karkala India

ISBN: (数字)9798350377972

ISBN: (纸本)9798350377972

Recognition of emotion in speech (RES) is generating significant interest due to its promise to improve human-computer interaction through precise identification of emotions from speech signals. This work investigates how well deep learning methods advance RES accuracy. The study makes it possible to analyze a variety of speech patterns, emotional expressions, and demographic information all of which are crucial for developing trustworthy RES models. According to the study, CNNs demonstrated their ability to capture spatial characteristics from voice spectrogram representations with an amazing accuracy rate of 93%. RNNs performed exceptionally well at modeling temporal dependencies and capturing the sequential nature of speech, with an accuracy rate of 90%. Both models performed well, but some emotions such 'angry' and 'surprise' were more accurately detected than others, including 'disgust' and 'happy,' suggesting room for improvement. © 2024 IEEE.

关键词： Adversarial machine learning

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Advances in Early Detection and Monitoring of Retinopathy in Preterm Infants Using CNN and MLP Models 7

Advances in Early Detection and Monitoring of Retinopathy in...

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7th International Conference on Circuit Power and Computing Technologies, ICCPCT 2024

作者： Hubert, G. Priscila, S. Silvia Bharath Institute of Higher Education and Research Department of Computer Applications Tamil Nadu Chennai India Bharath Institute of Higher Education and Research Department of Computer Science Tamil Nadu Chennai India

ISBN: (纸本)9798350372816

This research showcases significant advancements in the timely identification and monitoring of retinopathy among preterm infants through the integration of Multilayer Perceptron (MLP) and Convolutional Neural Network (CNN) models. We eradicate the necessity for subjective assessments by ensuring a precise and automated diagnosis through the implementation of these contemporary models. The CNN is capable of extracting robust features from retinal images, and the MLP model enhances the predictive precision and monitoring functionalities even further. By integrating these models, our approach enhances the precision and velocity of retinopathy diagnosis, thereby resulting in improved therapeutic outcomes. The proposed framework significantly improves neonatal care by incorporating cutting-edge technology that enables continuous and advanced detection of retinopathy in premature infants. © 2024 IEEE.

关键词： Convolutional neural networks

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A Comparative Study in Image Fusion Using Orthogonal and Biorthogonal Wavelet 12th

A Comparative Study in Image Fusion Using Orthogonal and Bio...

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12th International Conference on Soft Computing for Problem Solving, SocProS 2023

作者： Pandy, Saroj Kumar Kumar, Ankit Kumar, Gaurav Singh, Kamred Udham Singh, Teekam Srivastava, Sandeep Choudhury, Tanupriya Department of Computer Engineering and Applications GLA University UP Mathura India School of Computing Graphic Era Hill University Dehradun India Department of Computer Science and Engineering Graphic Era Deemed to be University Dehradun India Department of Computer Science and Engineering Galgotias University Greater Noida India Department of Computer Science and Engineering University of Petroleum and Energy Studies Dehradun India

ISBN: (纸本)9789819732913

A method called picture fusion is largely concerned with improving photographs to enhance scene visualization. In order to produce a composite image that is more significant and instructive, it attempts to maintain the key elements from each image. This method has been used in a number of industries, including robots, satellite images, and medicine. Researcher decision-making and the success of scientific endeavors are greatly influenced by the quality of the fused image. In this article, a comparison of the wavelets Haar and Bior 2.2 is made. In order to compare the two wavelets, different photos are employed. Based on metrics like the Peak Signal-to-Noise Ratio (PSNR), Signal-to-Noise Ratio (SNR), Mean Squared Error (MSE), Structural Similarity Index (SSIM), and Standard Deviation (SD), the performance of the wavelets is assessed. Based on the results, it can be said that Bior 2.2 performs somewhat better than Haar in terms of picture fusion quality. We use orthogonal and biological wavelets to compare how well image fusion works. To fuse pictures, we utilize the Discrete Wavelet Transform (DWT) and the maximum selection criteria. The performance of the fused pictures is assessed using a number of factors, such as visual quality measurements. The advantages of biorthogonal wavelets over orthogonal wavelets for image fusion tasks are highlighted in this research, especially when working with numerous sensors. It emphasizes how important the decomposition level is for producing high-quality fused pictures. The results help advance image fusion methodologies and offer insightful information to academics and industry professionals involved in picture processing and analysis. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Discrete wavelet transforms

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Coupling Mobile Brain Imaging and Virtual Reality Omnidirectional Treadmill to Explore Attenuated Situational Awareness during Distracted Walking

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IEEE Transactions on Cognitive and Developmental Systems 2024年第3期16卷 1063-1076页

作者： Chuang, Chun-Hsiang Chiu, Tsai-Feng Hsu, Hao-Che Lin, Shih-Syun National Tsing Hua University Research Center for Education and Mind Sciences College of Education The Institute of Information Systems and Applications College of Electrical Engineering and Computer Science Hsinchu300 Taiwan National Tsing Hua University Research Center for Education and Mind Sciences Hsinchu300 Taiwan National Yang Ming Chiao Tung University Department of Computer Science Hsinchu300193 Taiwan National Taiwan Ocean University Department of Computer Science and Engineering Keelung20224 Taiwan

Walking while engaging in distracting activities affects our perception of changes happening on the road, leading to exposure to hazardous conditions. This study aimed to evidence that pedestrians who become distracted when absorbed in their smartphones fail to maintain situational awareness. Thirty-six recruited participants performed a psychomotor vigilance task and a wayfinding task designed to simulate a multitasking scenario;meanwhile, an auditory oddball paradigm presented to participants tested their situational awareness of an external stimulus. The experiment was conducted in virtual reality (VR) environment with an omnidirectional treadmill. Participant's behavioral and electroencephalographic (EEG) responses were measured and analyzed using a newly proposed brain network measure, phase locking distance (PLD), to capture multitasking-associated couplings between brain regions. The current study supports the notion that multitasking can lead to diminished behavioral performance and changes in walking patterns. Despite the increased interregional brain connectivity, which may be an adaptive response to heightened cognitive demands, the associated reduction in event-related EEG activity suggests competition for neural resources during multitasking. Moreover, the observed EEG activities could indicate additional information processing capacity limitations and provide evidence of decreased situational awareness during multitasking, thereby increasing susceptibility to potentially hazardous situations. © 2016 IEEE.

关键词： Electroencephalography

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A Cutting Edge Deep Learning Models for Paddy Leaf Disease Detection and Classification 3

A Cutting Edge Deep Learning Models for Paddy Leaf Disease D...

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3rd International Conference on Artificial Intelligence For Internet of Things, AIIoT 2024

作者： Dhanalakshmi, A. Ponmozhi, K. Ajmal, A. Kalasalingam Academy of Research and Education Department of Computer Applications Tamil Nadu KrishnanKovil India Department of Data Science Karanataka Bangalore India

ISBN: (纸本)9798350372120

The world's main grain crop is rice (Oryza sativa). It provides the majority of the world's population with nourishment and energy. Numerous rice leaf diseases are a major threat to the world's agricultural output because they deteriorate crop quality in addition to lowering crop yield. Traditional methods of classifying and managing diseases mostly rely on laborious and subjective manual examination procedures. However, recent advancements in computer vision methods have made it possible to construct automated systems that can recognize illnesses from picture and video data. The paper emphasizes the potential application of cutting-edge models like Detectron2 to address issues with agricultural leaf disease detection. Together with other cutting-edge models, Detectron2 provides a promising means of enhancing the accuracy and effectiveness of disease diagnosis and control techniques. The use of automated disease detection systems holds the potential to reduce subjectivity in the evaluation of illnesses, optimize the allocation of resources in agriculture, and reduce dependence on human labour. These systems are able to continually learn from and adapt to changing disease patterns by incorporating complex algorithms and machine learning techniques, ensuring robust and stable agricultural operations. © 2024 IEEE.

关键词： Diseases

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