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machine and Deep learning Technique for Depression Detection Using EEG Data 7th

Machine and Deep Learning Technique for Depression Detection...

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7th international conference on Microelectronics, Electromagnetics and Telecommunication, ICMEET 2022

作者： Tiwari, Shikha Pandey, Kiran Sharma, Vivek Verma, Bhupendra Department of CSE Technocrats Institute of Technology Bhopal India

ISBN: (纸本)9789811988646

Electroencephalogram (EEG) signal-based emotion recognition has attracted wide interests in recent years and has been broadly adopted in medical, affective computing, and other relevant fields. Depression has become a leading mental disorder worldwide. Evidence has shown that subjects with depression exhibit different spatial responses in neurophysiologic signals from the healthy controls when they are exposed to positive and negative. Depression is a common reason for an increase in suicide cases worldwide. EEG plays an important role in e-healthcare systems, especially in the mental healthcare area, where constant and unobtrusive monitoring is desirable. EEG signals can reflect activities of the human brain and represent different emotional states. Mental stress has become a social issue and could become a cause of functional disability during routine work. this paper proposed an adaptive approach based on machine and deep learning for detecting depression using EEG. the algorithm first extracts features from EEG signals and classifies emotions using machine and deep learning techniques, in which different parts of a trial are used to train the proposed model and assess its impact on emotion recognition results. © 2023, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Electroencephalography

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Revolutionizing e-learning: Harnessing the Power of Artificial Neural Networks for Personalized Education and Beyond 1

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7th international conference on Intelligent computing and Optimization, ICO 2023

作者： Shard Kumar, Devesh Vaidya, Pankaj Koul, Sapna Faculty of Engineering and Technology Shoolini University Bajhol H.P Solan India School of Commerce and Management Studies Central University of Himachal Pradesh Dharamshala India Faculty of Applied Sciences and Biotechnology Shoolini University Bajhol H.P Solan India

ISBN: (数字)9783031733185

ISBN: (纸本)9783031733178

Artificial Neural Networks (ANNs) have emerged as a transformative technology in e-learning, revolutionizing how educational content is delivered and learners are supported. ANNs, inspired by the human brain, enable machines to learn from data, recognize patterns, and make predictions. In e-learning, ANNs find various applications such as personalized learning, intelligent tutoring, content recommendation, natural language processing, automatic grading, and predictive analytics. By analyzing learner data, ANNs can adapt instructional content, provide real-time guidance, recommend relevant resources, process natural language input, automate grading, and identify at-risk learners. Locating the best-customized learning resources online might be challenging for users and learners. By providing suggestions to users and learners based on their preferences and doing all of this with less time and effort, artificial neural networks significantly impact users’ ability to personalize their learning interests from a huge variety of resources. this study explores the value of employing neural networks in customized e-learning and demonstrates some of its existing uses, along with their advantages and drawbacks. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Adversarial machine learning

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Distributed Training of Large-Scale Deep learning Models in Commodity Hardware 7th

Distributed Training of Large-Scale Deep Learning Models in ...

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7th international conference on Inventive Systems and Control, ICISC 2023

作者： Ahmad, Jubaer Navin, Tahsin Elahi Al Awsaf, Fahim Arafat, Md. Yasir Hossain, Md. Shahadat Islam, Md. Motaharul Department of Computer Science and Engineering United International University Dhaka Bangladesh

ISBN: (纸本)9789819916238

Running deep learning models on a computer is often resource intensive and time-consuming. Deep learning models require high-performance GPUs to train on big data. It might take days and months to train models with large datasets, even with the help of high-performance GPUs. this paper provides an affordable solution for executing models within a reasonable time interval. We propose a system which is perfect to distribute large-scale deep learning models in commodity hardware. Our model consists of creating distributed computing clusters using only open-source software which can provide comparable performance to High-Performance computing clusters even with the absence of GPUs. Hadoop clusters are created by connecting servers with a SSH network to interconnect computers and enable continuous data transfer between them. We then set up Apache Spark on our Hadoop cluster. then, we run BigDL on top of Spark. It is a high-performance Spark library that helps us scale to massive datasets. BigDL helps us run large deep learning models locally in Jupyter Notebook and simplifies cluster computing and resource management. this environment provides computation performance up to 70% faster than a single machine execution with the option of scaling in case of model training, data throughput, hyperparameter search, and resource utilization. © 2023, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Data transfer

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Tomato Leaf Disease Detection through machine learning based Parallel Convolutional Neural Networks 7

Tomato Leaf Disease Detection through Machine Learning based...

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7th international conference on Intelligent computing and Control Systems, ICICCS 2023

作者： Shetty, Sahana Selvanayaki, S. Sumathi, T. Nageswari, C.Shobana Nisha Devi, K. Suganyadevi, S. FET-Jain Department of CSE-AIML Cyber Security School of CSE Jayanagar Bangalore India Saveetha Engineering College Department of Artificial Intelligence and Data Science Chennai India Dr.Mahalingam College of Engineering and Technology Department of Information Technology Pollachi India R.M.D. Engineering College Department of ECE Kavaraipettai601206 India Bannari Amman Institute of Technology Department: Artificial Intelligence and Data Science Sathyamangalam India KPR Institute of Engineering and Technology Department of ECE Coimbatore India

ISBN: (纸本)9798350397253

Agriculture is the primary economic activity in a significant number of nations. Yet, plant-related illnesses are the most difficult obstacle for farmers to overcome. the leaf is the portion of the plant that is noticed the most, but the process of segmenting an image of a sick lesion on a leaf suffers from challenges related to uneven lighting and a crowded, complicated natural environment. the combination of colour balancing and superpixel is the foundation of the approach that has been suggested as a solution to these issues. To begin, the picture that is being read in will be converted into a color-balanced image so that the effects of uneven lighting may be removed. Second, using the superpixel operation, compact areas are produced from the modified picture. An experimentally calculated threshold is then applied to the Histogram of Gradients (HOG) and colour channels of the superpixel to separate the undesirable backdrop from the image of the leaf. In order to identify a sick or contaminated picture, K-means clustering is employed. Pyramid of HOG (PHOG), an expanded version of HOG, together with Grey Level Co-occurrence Matrix (GLCM) characteristics are used to depict a sick infected portion of the body. In the end, many disease classifiers are evaluated against one another, and Random Forest (RF) is chosen as the best option. An experiment is carried out to verify the effectiveness of the proposed approach's accuracy using natural photographs obtained from a variety of farms, images downloaded from the internet, and the Plant-Village colour dataset. this is done so that the resilience of the method can be evaluated. © 2023 IEEE.

关键词： Color

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A Comparative Study of AI Algorithms for Anomaly-based Intrusion Detection 7

A Comparative Study of AI Algorithms for Anomaly-based Intru...

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7th international conference on computing Methodologies and Communication, ICCMC 2023

作者： Gandi, Vishnu Priya Jatla, Nikhil Sai Lalith Sadhineni, Gayathri Geddamuri, Sanjeev Chaitanya, Gogineni Krishna Velmurugan, A.K. Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram Guntur522302 India

ISBN: (纸本)9781665464086

the internet's quick development has brought numerous advantages, but it has also had some negative effects. Some of the drawbacks of the internet's incredible growth includes Cybersecurity risks, the internet has made it easier for hackers to access sensitive information, leading to an increase in cyber-attacks and data breaches. Privacy concerns, the internet has made it easier for companies to collect and use personal data, which has raised concerns about privacy. Disinformation, the internet has made it easier to spread misinformation and disinformation, which can have serious consequences for individuals and society. Intrusion detection systems (IDS) are important in the real world because they can help detect and prevent cyber-attacks, which can have serious consequences for individuals, organizations, and governments. Some of the benefits of IDS in the real world are Early Detection, IDS can detect suspicious activity or policy violations as they happen, allowing for early intervention and preventing attacks from causing significant harm. Alerting, IDS can alert security administrators about potential threats, enabling them to take appropriate action. Automated Response, IDS can be configured to take automated action when it detects a threat, such as blocking the source of the attack or isolating the affected system. Compliance, IDS can help organizations meet regulatory compliance requirements by providing visibility into network activity and detecting potential policy violations. Improved Security, by detecting and preventing attacks, IDS can improve overall security and reduce the risk of statistics breaches and other cyber threats. Overall, IDS is a crucial tool for maintaining the security of a computer system or network in the real world. the major objective of this study is to provide an in-depth analysis of recent trends and advancements in ML and DL-based NIDS solutions. © 2023 IEEE.

关键词： machine learning

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A Study on Comparative Analysis of machine learning and Image Processing Techniques for Identifying Plant Leaf Disease 7th

A Study on Comparative Analysis of Machine Learning and Imag...

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7th international conference on Smart Trends in computing and Communications, SmartCom 2023

作者： Badami, Vittal Kanakaraddi, Suvarna Kalwad, Priyadarshini K L E Technological University Karnataka Hubballi India

ISBN: (纸本)9789819908370

this research investigates the approaches for identifying and classifying plant leaf diseases from digital images using deep neural networks. While diseases can affect any part of a plant and occasionally go undetected, there are some physical characteristics or symptoms that the plants exhibit that can be quickly detected using vision. these diseases can be identified and classified by machine learning algorithms using a wide range of techniques like deep neural networks, regression analysis, colour analysis and so on but not all techniques are appropriate for identifying all types of plant leaf diseases. It might be difficult to determine which approach is best for particular type of disease detection. this paper mainly focuses on image classification based on deep neural networks, object detection based on thresholding and image transformation and severity quantification with different methods like colour analysis which include Histogram of Oriented Gradients (HOG) and spot analysis and leaf area measurement using open source tool and segmentation-based calculation of affected area. this detailed analysis will be helpful to researchers working on algorithm optimization for image classification, object detection and severity quantification in identifying plant leaf diseases. © 2023, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Computer vision

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A machine learning-Based Approach for Enhancing Student learning Experiences 5th

A Machine Learning-Based Approach for Enhancing Student Lear...

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5th international conference on soft computing and Signal Processing, ICSCSP 2022

作者： Bhaskar, T. Tribhuvan, R.R. Department of Computer Engineering Sanjivani College of Engineering Maharashtra Kopargaon India

ISBN: (纸本)9789811986680

the tried-and-true learning method of outcome-based learning (OBL) is based on a specified set of goals. there are three parts to OBL: educational objectives, program outcomes, and course outcomes (COs). At the end of each course, faculty members may take several ML-based recommended actions to enhance the quality of learning and instruction. Some students may be encouraged to engage in dangerous activities due to the sheer volume of classes and professors engaged. As a consequence of this research, multiple machine learning algorithms may be used to predict the correct course of action based on college course requirements and academic records. Different issue change strategies and versatile methodologies, for example, one-versus-all, parallel importance, naming power set, series arrangement, and custom order ML-KNN, might be utilized to a dataset to tackle the issue. At the Institute of Computer and Information Sciences, the suggested recommender ML-based framework is used as a contextual investigation to help academic staff improve learning quality and instructional approaches. According to the findings, the recommended recommendation method provides a greater number of options for enhancing students’ educational experiences. © 2023, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Students

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Spear Phishing Using machine learning 7th

Spear Phishing Using Machine Learning

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Proceedings of the 7th international conference on Advances in computing and Data Sciences, ICACDS 2023

作者： Hegde, Aditya Mahesh Kumar, S.P. Bharath Bhuvantej, R. Vyshak, R. Sarasvathi, V. Department of CSE Bengaluru PES University Bengaluru India

ISBN: (纸本)9783031379390

In this digital age, personal data is more valuable than an actual human being. It corresponds to a person’s daily internet activity. Open-source intelligence can be used to collect data in a variety of ways, which is later put to use for social engineering attacks. the purpose of this paper is to assess the level of data that is freely available online and its potential consequences for personal privacy. this paper discusses the use of machine learning algorithms and tools for automating email phishing attacks. the personal data of the victim is gathered from open-source websites in order to analyze their online activity. this information is then analyzed in an attempt to learn more about the victim’s interests and a relevant email template is created based on this information. the machine learning algorithm is then provided with the constructed template, which predicts how successful a phishing attack would be if launched. this paper uses a machine learning algorithm that is composed of support vector machines (SVM) and logistic regression (LR). this hybrid algorithm, which is a combination of SVM and logistic regression, achieves a peak accuracy of 99.69% when compared to using only one type of classification method, such as SVM or LR. the purpose of this paper is to increase the effectiveness of phishing attacks by automating the data extraction process and to analyse the success rate of attack using machine learning before launching it. this paper will serve the interests of institutions/companies by providing a convenient way to conduct automatic phishing as part of Cyber-Security training to educate employees, giving them a practical experience of social engineering attack. © 2023, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Python

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Enhancing Human Stress Detection from Optimized and Selected Features Using Feed Forward Neural Network 7th

Enhancing Human Stress Detection from Optimized and Selected...

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7th international conference on Intelligent computing and Optimization, ICO 2023

作者： Habiba, Sultana Umme Mahmud, Tanjim Islam, Md. Khairul Tasnim, Farzana Nahar, Lutfun Hossain, Mohammad Shahadat Andersson, Karl Khulna University of Engineering & Technology Khulna Bangladesh Rangamati Science and Technology University Rangamati Bangladesh International Islamic University Chittagong Sonaichhari Bangladesh University of Chittagong Chittagong Bangladesh Luleå University of Technology Luleå Sweden

ISBN: (纸本)9783031733239

Stress, a psycho-physiological response to life’s challenges and changes, poses significant health risks when chronic. this study explores the link between stress and sleep quality, visualizing how stress affects the human body. Stress is categorized into five levels, and eight key physiological parameters are analyzed. machine learning algorithms, including Naïve Bayes, Support Vector machine, and Random Forest Classifier, achieved an impressive 100% accuracy in stress level classification. Further enhancements using Principal Component Analysis (PCA) and Chi-square tests identified optimal features, resulting in approximately 99.68% accuracy with a feed-forward neural network (Multilayer Perceptron). these findings advance stress detection and its impact on sleep quality, offering potential applications in real-world health monitoring systems. the model’s high accuracy provides valuable insights for stress management and preventive health measures, mitigating associated risks. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Multilayer neural networks

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machine learning and Cloud-based Mobile App for Real Time Skin Cancer Prediction 7

Machine Learning and Cloud-based Mobile App for Real Time Sk...

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7th international conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2023

作者： Kaushal, Rajanish Kumar Hemalakshmi, G.R. Prakash, N.B. Maruthai, Suresh Gupta, Keerat Kumar Natrayan, L. Chandigarh University Department of Electrical Engineering Punjab Mohali India Vit Bhopal University School of Computing Science and Engineering Madhya Pradesh 466114 India National Engineering College Kovilpatti Department of Electrical and Electronics Engineering Tamil Nadu 628503 India St. Joseph's College of Engineering Department of Electronics and Communication Engineering Tamil Nandu Chennai600119 India Graphic Era Deemed to Be University Department of Civil Engineering Uttarakhand Dehradun248002 India Saveetha School of Engineering Simats Department of Mechanical Engineering Tamil Nadu Chennai602105 India

ISBN: (纸本)9798350341485

the most prevalent kind of cancer is skin cancer. If discovered quickly, this can be effectively treated. It is extremely expensive to diagnose the cancer in its initial stages. Skin cancer is the result of abnormal skin cell growth. When detected and treated promptly, these are highly curable. the implementation of machine learning (ML) Algorithms in collaboration with Mobile Applications helps to automate the skin cancer prediction system. In this study, 2357 images of actinic keratosis, dermatofibroma, basal cell carcinoma, nevus, squamous cell carcinoma, pigmented benign keratosis, vascular lesion diseases, seborrheic keratosis, and melanoma, are taken from the Kaggle site. First, the images are converted to grayscale. the Median Filter data pre-processing technique is employed to clean the data. the feature extraction (FE) methods include Discrete Wavelet Transform (DWT). this method aids in facilitating the extraction of useful information from data. then, the discovered attributes will be fed into an ML classifier, such as Naïve Bayes (NB), Linear Discriminant Analysis (LDA), and Random Forest (RF) to build the model. Several metrics for performance, particularly accuracy, True Positive Rate (TPR), True Negative Rate (TNR), precision, and F1 score (F1), are used in assessing FE and categorization. the research reveals that the NB model is the best combination, providing an accuracy level of 96.61%. Finally, the NB model is deployed in real-time to create a skin cancer detection app using MIT App Inventor. © 2023 IEEE.

关键词： Discrete wavelet transforms

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