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Detection and Treatment of Epilepsy Disease using EEG Signals

Detection and Treatment of Epilepsy Disease using EEG Signal...

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2024 International Conference on Cybernation and Computation, CYBERCOM 2024

作者： Dhondiyal, Shiv Ashish Dimri, Sushil Chandra Graphic Era Deemed to Be University Dept. of Computer Science and Engineering Dehradun India

ISBN: (纸本)9798331542344

Epilepsy Seizures is a neurological illness that causes repeated seizures and affects millions of people throughout the world. From the studies it has been detected that 70% peoples in the world suffer from epilepsy disease. The prognosis and medical care of epileptic seizure are critical for improving the quality of life of affected individual. Electroencephalography (EEG) is a valuable technique for diagnosing and managing epilepsy because it allows for real-time monitoring of brain electrical activity. Recent advancements in signal processing techniques, such as the Discrete Wavelet Transform (DWT), have shown significant promise in enhancing the diagnosis and treatment of epilepsy. This paper purposed an optimize Random Forest (RF) algorithm by using the DWT application in the analysis of EEG signals for the prognosis of epilepsy disease along with its treatment like medicines, therapies and other treatments methods to cure epilepsy disease. The suggested approach provides clinicians with a promising tool for early and precise identification of epileptic seizures, which is critical for making improvements in patient outcomes and quality of life. The simulation results show that the purposed optimize support vector machine algorithm gives better results with an accuracy of 98% along with the sensitivity, specificity and f1-score of 97%, 96% and 98% respectively. © 2024 IEEE.

关键词： Electroencephalography

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Plant Disease Detection using Convolutional Neural Network

Plant Disease Detection using Convolutional Neural Network

引用

2024 International Conference on Smart Devices, ICSD 2024

作者： Agarwal, Jyoti Naithani, Shivansh Singh, Utkarsh Raj, Harsh Graphic Era Deemed to be University Dept. of Computer Science and Engineering Dehradun India

ISBN: (纸本)9798350394412

Pest-infested plants and crops have a negative effect on the nation's agricultural output. To detect and identify diseases in plants, farmers or other experts must keep a careful watch on plants. However, this method is frequently expensive, time-consuming, and inaccurate. Finding a spot on the leaves of the infected plant is one method of detecting plant diseases. This research aims to develop a disease detection model based on the classification of leaf images. To detect plant illnesses the proposed model utilizes image processing with a convolution neural network (CNN). This research work focus on three different plant diseases: tomato, corn and potato. Experimental results show high accuracy rate of 98.8%. Proposed work is also compared with some existing work working o same plants diseases. © 2024 IEEE.

关键词： Support vector regression

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MAD: A Meta-Learning Approach to Detect Advanced Persistent Threats using Provenance Data in Industrial IoT 40

MAD: A Meta-Learning Approach to Detect Advanced Persistent ...

引用

40th Annual computer Security Applications Conference Workshops, ACSACW 2024

作者： Saha, Bikash Rani, Nanda Shukla, Sandeep Kumar Indian Institute of Technology Kanpur Dept. of Computer Science and Engineering Kanpur India

ISBN: (纸本)9798331532819

The increasing reliance on Industrial Internet of Things (IIoT) systems in critical infrastructure has made these environments prime targets for stealthy and prolonged Advanced Persistent Threats (APTs) that evade traditional security systems. This study introduces MAD (Meta-learning based APT Detection), a novel framework designed to detect APTs in IIoT environments by leveraging provenance data. This data provides a detailed view of system-level interactions, capturing subtle and persistent malicious behaviors. We evaluate MAD on the CICAPT-IIoT dataset, which simulates real-world APT scenarios with severe class imbalance due to the "low and slow"nature of APT attacks. MAD includes two variants: MAD v1, utilizing a multi-layer perceptron (MLP), and MAD v2, employing Model-Agnostic Meta-Learning (MAML) to improve detection of rare attack behaviors. MAD v1 achieves F1-score of 0.91 and outperforms the existing baselines. MAD v2 achieves F1-score of 0.95 surpassess existing baselines as well as MAD v1, which demonstrates its enhanced capability to detect rare APT activities despite the dominance of benign samples. This work combines provenance-based detection with meta-learning to address class imbalance and enhance APT detection in IIoT, demonstrating MAD's potential in tackling emerging IIoT security challenges. © 2024 IEEE.

关键词： Industrial internet of things (IIoT)

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Enhanced Detection of DoS/DDoS Attacks in SDN Using Ensemble and Hybrid CNN-LSTM Models

Enhanced Detection of DoS/DDoS Attacks in SDN Using Ensemble...

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

作者： Afroj, Maharin Rifat, Khan Md Sadbin Rahman, Md. Saifur Bangladesh University of Business and Technology Dept. of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9798331529765

Software-Defined Networking (SDN) faces significant challenges in mitigating Attacks known as denial of service (DoS) and distributed DDoS due to its centralized control plane architecture. This centralization makes the control plane an attractive target for attackers, highlighting the need for effective detection and mitigation strategies. In this study, we propose three innovative methodologies to enhance the accuracy of detecting DoS/DDoS attacks in SDN environments. Our first methodology employs an ensemble model with a voting classifier, which achieves a detection accuracy of 97.4%. This approach leverages multiple weak learners to enhance the robustness of detection. The second methodology utilizes an ensemble with a stacking technique, attaining a superior accuracy of 99%, demonstrating the efficacy of integrating multiple base models in a hierarchical fashion. The third approach introduces a novel hybrid model that blends LSTM networks with CNN, achieving a competitive accuracy of 98.51%. A comparative analysis of these methods reveals that, while all three significantly enhance detection rates, the stacking ensemble technique outperforms the others. This finding underscores the potential of advanced ensemble techniques in effectively addressing the dynamic and evolving cyber threats within SDN environments. Our study offers valuable insights into strengthening SDN infrastructures against DoS/DDoS attacks, contributing to the ongoing efforts to secure modern network architectures. © 2024 IEEE.

关键词： Denial-of-service attack

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Improving Cardiac Arrest Prediction in PICU Through Gradient Boosting and Neural Networks

Improving Cardiac Arrest Prediction in PICU Through Gradient...

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2024 Asian Conference on Intelligent Technologies, ACOIT 2024

作者： Viswanath, Ch. Srujan Raju, K. Nuthanakanti, Bhaskar Dept. Computer Science and Engineering CMR Technical Campus Telangana Hyderabad India

ISBN: (纸本)9798350374933

Improving PICU patient outcomes after a cardiac arrest (CA) requires an accurate and timely prognosis. The purpose of this study is to concentrate on the use of machine learning (ML) approaches for the prediction of cardiac arrest in pediatric critical care units. We identified critical predictive parameters and evaluated the efficacy of several ML approaches after conducting a thorough literature assessment of recent advances in this sector. Vital signs, test findings, and demographic information were all part of the electronic health records (EHRs) that made up our dataset, which came from several PICUs. Heart rate, breathing rate, blood pressure, oxygen saturation, and electrocardiogram transmissions are some of the comprehensive physiological data provided by neonates and babies in the cardiac critical care unit. A variety of machine learning approaches were trained and evaluated using metrics such as sensitivity, specificity, recall, F1 score, and ROC-AUC. A few examples of these algorithms include Gradient Boosting, Support Vector Machine (SVM), Random Forest, and neural networks. Among the measures examined, the Gradient Boosting model emerged as the frontrunner, with an impressive ROC-AUC of 0.95, a recall of 91.0%, a specificity of 93.5%, an accuracy of 89.7%, and an F1 score of 90.3%. Blood pressure trends and heart rate variability were identified as key predictors using feature importance analysis. These findings demonstrate that ML can improve clinical decision-making and decrease the occurrence of cardiac arrests in children. © 2024 IEEE.

关键词： cardiac arrest cardiovascular ICU early identification infants machine learning neonates physiological data

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Cloud-Based Water Parameter Monitoring for Smart Fisheries

Cloud-Based Water Parameter Monitoring for Smart Fisheries

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2024 IEEE Region 10 Symposium, TENSYMP 2024

作者： Bahadur, Arshad Mohammad Sakib, Najmus Moinuddin, Kazi Md. Hussein Islam, Tanzimul Islam, A.K.M. Muzahidul United International University Dept. of Computer Science & Engineering Dhaka1212 Bangladesh

ISBN: (纸本)9798350364866

The Cloud-based Water Parameter Monitoring System leverages cloud and IoT technologies for the precise monitoring of water quality across various water bodies. In Bangladesh, fish is a vital dietary component, consumed more frequently and in larger quantities than any other animal-source food. The fisheries sector greatly contributes to GDP, employment, and it is the second-largest export product of the country. However, pollution and inadequate real-time water quality monitoring significantly impact the industry, costing thousands of crores. This research involved collecting water parameter data from different water bodies using specialized sensors connected to Arduino devices. The data was processed and stored on an edge server before being periodically sent to a cloud server. We developed a user-friendly web application and integrated an alert system to notify farm workers when parameters deviate from acceptable ranges. The goal is to enhance sustainable fish farming practices by improving operational efficiency, reducing environmental impact, and ensuring optimal fish health through IoT, Machine Learning techniques, and Cloud technologies. © 2024 IEEE.

关键词： Fish

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Reconnaissance Detection in Decentralised IoT System using Model-Contrastive Federated Learning 18

Reconnaissance Detection in Decentralised IoT System using M...

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18th IEEE International Conference on Advanced Networks and Telecommunications Systems, ANTS 2024

作者： Dutta, Rajdeep Kumar Chhetry, Bishal Matam, Rakesh Barbhuiya, Ferdous Ahmed Indian Institute of Information Technology Dept. of Computer Science and Engineering Guwahati India

ISBN: (纸本)9798350391725

The detection of reconnaissance attacks is crucial for safeguarding Internet of Things (IoT) environments, which are inherently more vulnerable and resource-constrained compared to traditional computing systems. Traditional centralised detection methods face significant challenges, such as privacy concerns and limited scalability due to the need to aggregate raw data on a central server. These issues become particularly pronounced in IoT environments where devices are diverse and geographically distributed. To address these challenges, we propose ReconGuard, a novel approach leveraging Model Contrastive Federated Learning (MCFL). ReconGuard enables collaborative training across multiple IoT devices without the necessity of sharing raw data, thereby preserving user privacy and enhancing data security. The approach integrates contrastive learning techniques, which improve the model's ability to discriminate between benign and malicious activities by contrasting positive (similar) and negative (dissimilar) data pairs. Our experimental results demonstrate that the ReconGuard-based detection system provides a scalable and privacy-preserving solution for identifying reconnaissance activities. These activities are often the initial step in more severe cyber threats, such as botnet attacks, which can lead to significant disruption and damage. By effectively detecting reconnaissance activities, ReconGuard enhances the overall cybersecurity framework of IoT environments. This research presents a viable and innovative method for improving cybersecurity in IoT systems, addressing the critical need for scalable, efficient, and privacy-preserving intrusion detection solutions. The use of MCFL in ReconGuard not only mitigates the challenges of data centralisation but also leverages the distributed nature of IoT networks to enhance robustness against cyber threats. © 2024 IEEE.

关键词： Contrastive Learning

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World Coin Currency Detection: A Comparative Study of Machine Learning and Deep Learning

World Coin Currency Detection: A Comparative Study of Machin...

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2024 International Conference on Smart Devices, ICSD 2024

作者： Dumka, Ankur Pandey, Arnav Mishra, Amit Kumar Bhaskarrao Bahadure, Nilesh Tripathi, Neha Pandey, Neeraj Kumar Women Institute of Technology Dept. of Computer Science & Engineering Dehradun India St. Joseph's Academy Dehradun India Graphic Era Hill University Dept. of Computer Science & Engineering Dehradun India Symbiosis Institute of Technology Nagpur Campus Pune India Dept. of Computer Science & Engineering Dehradun India

ISBN: (纸本)9798350394412

In the economy where trade and finance are closely linked it is essential to accurately identify and authenticate various currencies, especially in industries such, as banking, tourism and retail. Conventional methods, for identifying coins typically require inspections or simple automated processes, which might take up a lot of time and are susceptible, to mistakes. The advancements, in machine learning (ML) and deep learning (DL) technologies, over the years have revolutionized currency detection providing efficient solutions. This article discusses the evaluation of machine learning and deep learning approaches to identify types of coins. It explores how algorithms, like MobileNetV2, Support Vector Machines (SVM), Random Forest, Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN) perform in distinguishing and classifying coin denominations across currencies. The results of experiments conducted on data sets show how these methods compare in terms of accuracy, effectiveness, and reliability. Furthermore, we examine the advantages and constraints of each approach while suggesting directions for advancing research, in the realm of identifying cryptocurrency. © 2024 IEEE.

关键词： Convolutional neural networks

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Pruning Based Improved Pedestrian Detection in Video Surveillance 11

Pruning Based Improved Pedestrian Detection in Video Surveil...

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11th International Conference on Reliability, Infocom Technologies and Optimization, ICRITO 2024

作者： Sharma, Suchi Diwakar, Manoj Gangodkar, Durgaprasad Graphic Era Deemed to Be University Dept. of Computer Science and Engineering Dehradun India

ISBN: (纸本)9798350350357

Real-time object identification applications are using methods for image processing more and more as a result of advancements in computer technology. Results for pedestrian identification were improved by the use of deep learning techniques. To locate human beings quickly and precisely, this study suggests employing pruning to increase the Yolo models' efficiency in pedestrian recognition. This method would allow the device to function seamlessly when used for video surveillance. This method uses one stage detector, YOLOv5, to better suit the task situations and adjust to the real-time demands. To improve the possible model sparsity, 1 × 1 kernel weights are pruned using a pruning approach. The findings show that, following pruning, this model attains a 30% sparsity in this model, which indicates that 30% of the weight parameters in the nn. Conv2d layers of the algorithm are equal to 0. Inference time is nearly constant and soft-nms is applied for better ROI results. In addition, a thorough result and discussion are given to demonstrate the efficacy of this methodology regarding mAP, latency, energy consumption, and attained sparsity. © 2024 IEEE.

关键词： Energy utilization

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Analyzing Cyberbullying Word Distribution Across Social Media to Develop Lexicon Dictionary 2

Analyzing Cyberbullying Word Distribution Across Social Medi...

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

作者： Sahana, V. Anil Kumar, K.M. Jss Academy of Technical Education Dept. of Information Science and Engineering Bengaluru560060 India Jss Science and Technology University Dept. of Computer Science and Engineering Mysuru570006 India

ISBN: (纸本)9798350372892

Social media platforms have become essential tools for communication, collaboration, and exchanging information, ideas, and knowledge among users worldwide. Despite their benefits, the anonymity offered by these platforms has unfortunately led to a rise in hate speech and cyberbullying, causing concern across the globe. This issue has drawn the attention of researchers and scholars now focused on devising methods for automatically detecting cyberaggression and hate speech. The goal is to mitigate these harmful behaviors and create safer online environments [1]. This study specifically aims to identify and analyze the usage of cyberbullying language within the English Tweet corpus, focusing on distinguishing between bullying and non-bullying language. This research contributes to developing more effective cyberbullying detection models by examining the frequency and patterns of cyberbullying words. These findings are vital for enhancing the efficacy of cyberbullying detection on social networks in future research efforts. The conclusions of the experiments indicate that the SVM achieved the highest average accuracy, approximately 84 %. © 2024 IEEE.

关键词： Anonymity

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