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Comparative Analysis of Epileptic Seizure Prediction: Exploring Diverse Pre-Processing Techniques and Machine Learning Models 10

Comparative Analysis of Epileptic Seizure Prediction: Explor...

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10th IEEE Uttar Pradesh Section International Conference on Electrical, Electronics and computer engineering, UPCON 2023

作者： Hasan Talukder, Md. Simul Sulaiman, Rejwan Bin Chhabra, Gunjan Yadav, Suman Avdhesh Haque, Rezaul Islam, Md Babul Kaushik, Keshav Nuclear Safety Security and Safegurad Division Bangladesh Atomic Energy Regulatory Authority Dhaka Bangladesh School of Computer Science and Technology Northumbria University Newcastle United Kingdom Graphic Era Hill University Dept. of Cse Dehradun India Amity School of Engineering & Technology Department of Computer Science and Engineering Noida India East West University Dept of Computer Science and Engineering Dhaka1212 Bangladesh Calabria Italy School of Computer Science University of Petroleum and Energy Studies Dehradun India

ISBN: (纸本)9798350382471

Precise prediction and detection of epilepsy, a common neurological condition marked by sporadic and repeated seizures, is necessary for efficient management and patient care. To do accurate and reliable seizure detection, machine learning (ML) on electroencephalogram (EEG) recordings is a good choice because of its capacity to provide insightful information about brain activity during seizures. In this work, we do a thorough comparative analysis of five machine learning methods for the prediction of epileptic seizures using EEG data: Random Forest (RF), Decision Tree (DT), Extra Trees (ET), Logistic Regression (LR), and Gradient Boosting (GB). Firstly, to assure the quality of the data and enable precise model training, the dataset underwent extensive preprocessing, which included cleaning, normalization, treatment of outliers, and oversampling. These preprocessing techniques significantly enhanced the models' performance. In our study, we evaluate the accuracy of each model;the LR classifier obtains 56.95% accuracy, while both GB and DT achieve 97. 17% accuracy. Outperforming the findings of previous investigations, the ET model showed the best performance with an accuracy of 99.29%. Achieving 98.99% accuracy was another accomplishment for the RF model. Our results imply that the ET model not only performed better than the other models in the comparison analysis but also more effectively than the most advanced outcomes from earlier research. Regarding the accurate and dependable prediction of epileptic seizures using EEG data, the ET model is a solid option due to its remarkable performance. © 2023 IEEE.

关键词： EEG epileptic seizure Extra Tree hyperparameter tuning machine learning

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Network Intrusion Detection for IoT Devices Using Deep Learning

Network Intrusion Detection for IoT Devices Using Deep Learn...

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IEEE UP Section Conference on Electrical computer and Electronics (UPCON)

作者： Mohit Kumar Sanjay Kumar Dubey Department of Computer Science and Engineering Amity School of Engineering and Technology Amity University Noida Uttar Pradesh India

Cybersecurity has seen widespread adoption across various domains, encompassing critical business infrastructure, residential settings, personal devices, and machinery. This has led to the development of innovative capabilities to effectively address security challenges within the realm of Internet of Things (IoT) devices. Notably, new security measures, including the application of deep learning for intrusion detection, have been introduced. Recent research primarily focuses on enhancing algorithms for a deeper understanding of IoT security. This research explores intrusion detection techniques employing deep knowledge mining. It involves a comprehensive analysis of various deep knowledge mining methods, comparing their overall performance. The objective is to identify a precise approach for implementing intrusion detection in the IoT context. The research leverages deep learning methodologies, specifically convolutional neural networks (CNN), long short-term memory (LSTM), and gated recurrent units (GRU). Furthermore, it incorporates an extensive dataset tailored for IoT intrusion detection. The empirical results of this study are thoroughly examined and compared against existing IoT intrusion detection techniques. Notably, the proposed approach demonstrates superior accuracy when compared to other methods.

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Revival of Fingerprint Recognition-based Biometric Systems

Revival of Fingerprint Recognition-based Biometric Systems

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International Conference on Parallel Distributed and Grid Computing (PDGC)

作者： Akshay Juneja Priyanka Kumari Monika Bharti Himanshu Jindal Girish Wadhwa Pirmohammad Khan Amity School of Engineering and Technology Amity University Punjab India Department of Electrical and Instrumentation Engineering Thapar Institute of Engieneering and Technology Punjab India Amity School of Engineering and Technology Amity University Punjab Mohali Punjab India Department of Computer Science and Engineering Parul University Vadodara Gujrat India

ISBN: (数字)9798331521349

ISBN: (纸本)9798331521356

Fingerprint is one of the oldest and most reliable biometric technique. This paper presents different types of fingerprint recognition techniques including contactless recognition, indoor positioning, browsing research, and liveliness detection. Contactless recognition eliminates the need of physical touch and improves user comfort and hygiene by using sophisticated approaches such as convolutional neural networks (CNNs) and minutiae-based algorithms for precise identification. Indoor positioning utilizes fingerprint to recognize an individual using machine learning methods, such k-nearest neighbors (kNN) and support vector machines (SVM). It correlates radio signals with fingerprints for accurate location monitoring. Another approach called browser fingerprint recognition identifies distinct browser settings, such that monitoring of dynamic attributes is performed. Liveness detection is used to differentiate forged fingerprint from genuine records during presentation assaults using texture analysis and deep learning algorithms. This paper presents the study of recent advancements in these domains, that emphasizes the key features and limitations of existing techniques in fingerprint-based biometric systems for future researchers.

关键词： Support vector machines Machine learning algorithms Heuristic algorithms Fingerprint recognition Nearest neighbor methods Grid computing Browsers Reliability Object recognition Monitoring

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Building a Chatbot using Natural Language Processing

Building a Chatbot using Natural Language Processing

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Informatics (ICI), International Conference on

作者： Uday Sehgal Shweta Bhardwaj Department of Computer Science and Engineering Amity School of Engineering and Technology Amity University Uttar Pradesh Noida India

Chatbots are applications which simulate human conversational patterns, engage in human-to-human communication, and respond to user queries using data from their training set. There are two categories of chatbots: rule-based and self-learning. One of the beneficial things a company or organisation may have is a chatbot. They can be utilised for customer service and query resolution in many different industries. In this paper, research is done on NLP and a model is designed to build a chatbot using NLP techniques. The chatbot built using this model can be used as a normal conversation bot or as a customer support tool.

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Edge Intelligence in IoT: Architecture and Applications

Edge Intelligence in IoT: Architecture and Applications

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International Conference on Communication and Electronics Systems (ICCES)

作者： Kajal Jewani Satheesh Abimannan Department of Computer Science and Engineering Amity School of Engineering and Technology (ASET) Amity University Mumbai Maharashtra India

This research survey presents a comprehensive analysis of IoT architectures that incorporate edge computing, examining their classifications, applications, and the challenges and limitations associated with their implementation. The survey also explores future directions and trends in IoT architectures that leverage edge computing, including 5G integration, artificial intelligence (AI), and machine learning (ML) advancements, edge virtualization, edge security, and improved interoperability. The research study highlights the potential of IoT architectures that utilize edge computing to address the challenges posed by traditional cloud-centric architectures while also acknowledging the key challenges and limitations that need to be overcome. By examining the current state of the field and emerging trends and technologies, this survey aims to provide a valuable reference for researchers, practitioners, and industry stakeholders working on the development and implementation of IoT solutions incorporating edge computing.

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Development Of an Intelligent Traffic Navigation System by Wireless Sensor Network System

Development Of an Intelligent Traffic Navigation System by W...

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2023 International Conference on New Frontiers in Communication, Automation, Management and Security, ICCAMS 2023

作者： Kaushal, Rajanish Kumar Kumar, B.Anjanee Chauhan, Krupesh A. Munagala, Madhu Singh, Pavitar Parkash Hazela, Bramah Chandigarh University Department of Electrical Engineering Punjab Mohali India Dadi Institute of Engineering and Technology Anakapalle Electronics and Communication Engineering Visakhapatnam India Sardar Vallabhbhai National Institute of Technology Department of Civil Engineering Surat India Koneru Lakshmaiah Education Foundation Department of English Vaddeswaram India Lovely Professional University Department of Management Phagwara India Amity University Amity School of Engineering & Technology Department of Computer Science and Engineering Lucknow India

ISBN: (纸本)9798350317060

The use of automobiles is becoming more important worldwide, especially in densely populated cities. Current transportation infrastructure technologies include wired sensors, inductive loops, and satellite navigation. The installation and upkeep of surveillance equipment (cameras, etc.) are quite costly. In addition, environmental factors affect these instruments' precision. Traditional methods typically aim to optimize traffic light management for a given traffic density and layout. One key draws back all these methods, however, it may be challenging to represent the dynamic behaviour of traffic volumes and configurations, which is always changing. Therefore, in an effort to solve the aforementioned problem, this study proposes methods for adaptive traffic light management via the use of a distributed sensor network. The purpose of this study is to examine approaches to developing a smart system that can integrate and support some of the current technologies of traffic management, therefore decreasing the typical wait time for cars at a junction. The suggested algorithms are flexible enough to adjust to the particulars of traffic at every given crossroads. (GLD) is a traffic simulation tool used to model real-world traffic conditions and provide graphs of average waiting time vs cycle counts. The collected findings validate the efficacy of the suggested strategy for traffic regulation at a real-world road junction. © 2023 IEEE.

关键词： Wireless sensor networks

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Anxiety and Depression Detection using Machine Learning

Anxiety and Depression Detection using Machine Learning

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2022 International Conference on Machine Learning, Big Data, Cloud and Parallel Computing, COM-IT-CON 2022

作者： Aggarwal, Riya Goyal, Anjali Amity University Noida Computer Science and Engineering Uttar Pradesh India Sharda University School of Engineering and Technology Uttar Pradesh Greater Noida India

ISBN: (数字)9781665496025

ISBN: (纸本)9781665496025

Anxiety and Depression are among the extremely crucial mental health issues. Every year, millions of individuals suffer from depression and anxiety, yet only a small percentage of them receive timely treatment. Throughout the last few decades, the evolution of machine learning methods has considerably aided in the development of technologies that assist clinicians in predicting various sorts of mental illnesses. As the Covid-19 pandemic rapidly spreads over the world, the global gaming industry is booming. Youth is increasingly focused on digital games due to lockdown and social distancing policies. This work presents a system for predicting if a player suffers from psychological illnesses such as anxiety and depression by combining game and player information with a selfesteem measure. The game and player's data has been gathered from two questionnaires namely, GAD and SWL, and multiple state-of-the-art simulations have been conducted. Four different machine learning classifiers were tested using 10-fold cross-validation approach on data set of internet gamers. Among the four algorithms, Decision Tree classifier showed the best accuracy for all predicted parameters. For GAD and SWL questionnaires, decision tree obtains accuracy of 100% and 84.71% respectively. © 2022 IEEE.

关键词： Decision trees

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A New Hybrid Cuckoo Search for the Resources-Constrained Project Scheduling Problem

IAENG International Journal of Computer Science

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IAENG International Journal of computer science 2021年第2期48卷 1-12页

作者： Shen, Xin Zhang, Xiaoxia Yu, Ziqiao X. Shen is a Master Student of School of Computer Science and Software Engineering University of Science and Technology LiaoNing Anshan114051 China X. X. Zhang is a Professor of School of Computer Science and Software Engineering University of Science and Technology LiaoNing Anshan11041 China A Master Student of School of Computer Science and Software Engineering University of Science and Technology LiaoNing Anshan114051 China

Resource-constrained project scheduling problem (RCPSP) is a kind of representative practical engineering problem, the purpose is to schedule activities in the project through rational use of limited resources in the minimum time. This paper proposes an improved hybrid cuckoo algorithm (CS&SA) to solve the RCPSP problem. Firstly, the individual elements are randomly coded into priority vectors, and the population is decoded using serial scheduling to convert the individual into a set of task scheduling sequences. Secondly, the Levy flight is redesigned to change the algorithm from random walk to adaptive as the population fitness changes. Then, this article adds three neighborhood update techniques to meet the update requirements of the algorithm at different stages. Finally, in order to prevent the algorithm from falling into a local optimum, this paper introduces a simulated annealing strategy to allow the algorithm to accept some individuals with poor quality with a certain probability in each iteration. In the testing part, this paper firstly tests the effectiveness and optimization of three different-scale examples in the classic example library PSPLIB for RCPSP problems. Among them, the average error of the CS&SA algorithm in the small-scale J30 is 0.25%, and the average error in the medium-scale J60 is 11.21%, and the average error of the large-scale J120 is 19.83%. Then, by comparing other intelligent optimization algorithms, it is proved that NCS&SA is superior to other algorithms in optimization and accuracy. © 2021. All Rights Reserved.

关键词： Combinatorial optimization

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SEEMA: An Automation Framework for Vulnerability Assessement and Penetration Testing

SEEMA: An Automation Framework for Vulnerability Assessement...

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International Conference on Vision Towards Emerging Trends in Communication and Networking (ViTECoN)

作者： Yash Tyagi Shubham Shekhar Abhishek P Shweta Bhardwaj Department of Computer Science and Engineering Amity School of Engineering and Technology Amity University Noida Uttar Pradesh India

The world has witnessed an exponential increase in the number of Internet Connected computers due to various causes. This exponential increase and the resulting dependence have had far reaching cybersecurity implications. The primary implication being exponential rise in cybercrime which necessitates the vulnerability assessment and penetration testing (VAPT). The vulnerability assessment and penetration testing aids in offsetting any liability associated with a successful attack on an organization's asset. The current frameworks and tool available for VAPT require very high expert knowledge and are predominantly manual. Moreover, some tools available automate only certain phases of VAPT but not the whole process. This mismatch provides the opportunity to create a solution by automating the whole process while lowering the knowledge threshold. Thus, this paper provides a revamped automated framework for a novice individual and a cybersecurity professional's use. It also provides a framework for commercial use which leverages multi-agent system and certain aspects of full stack monitoring.

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Creativity: Mining of Innovative Thinking Using Educational Data

Creativity: Mining of Innovative Thinking Using Educational ...

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Disruptive Technologies (ICDT), International Conference on

作者： Ritambhara Shailendra Narayan Singh Computer Science & Engineering Amity University Noida Utter Pradesh India Department of Computer Science & Engineering Amity School of Engineering and Technology Amity University Noida Utter Pradesh India

Innovation i.e., the process of new ideas that are useful or effective in different types of domains. Innovation should be taught in schools, homes, and other extra places. Innovative will be increased or improved with the help of Education Data Mining (EDM), which will improve the quality of Information. By employing BERT- type, objectives are to train representation from sequence process data, then fine-tunes these representations on subsequent prediction tasks, by WEKA and machine learning algorithms. In addition, we investigated students’ performance in exams using machine learning and symmetry-based learning algorithms. This article highly predicts female students pass more than boys students.

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